hypothesis on cyber bullying

A Systematic Research Synthesis on Cyberbullying Interventions in the United States

Affiliation.

• 1 Department of Family and Child Sciences, Florida State University , Tallahassee, Florida.
• PMID: 30334647
• DOI: 10.1089/cyber.2018.0307

In a society where it is becoming more common for perpetrators to choose electronic forms of communication (cell phones, social media, etc.) to bully others, it is crucial that we understand how our country is working to intervene in this cyberbullying epidemic. Therefore, this systematic research synthesis sought to examine all intervention efforts addressing cyberbullying that have been implemented within the United States. A systematic search using variations of cyberbullying intervention program search terms was narrowed down to a final sample size of 11 articles fitting the inclusion and exclusion criteria. Results suggested that programs addressing cyberbullying have only been implemented in schools or online, and most have not been evaluated for their effect on actual cyberbullying behaviors, but rather on attitudes and intentions about cyberbullying. Despite the significant concern about cyberbullying and its potential problematic outcomes, there seems to be a glaring lack of effective evidence-based programs that have been implemented in the United States.

Keywords: bullying; cyber abuse; cyber safety; cyberbullying; cyberethics; intervention.

Publication types

• Systematic Review
• Crime Victims / psychology*
• Cyberbullying / prevention & control*
• Cyberbullying / psychology
• Social Media*
• United States

ORIGINAL RESEARCH article

Study of the influencing factors of cyberbullying among chinese college students incorporated with digital citizenship: from the perspective of individual students.

• School of Educational Information Technology, South China Normal University, Guangzhou, China

Understanding the influencing factors of cyberbullying is key to effectively curbing cyberbullying. Among the various factors, this study focused on the personal level of individual students and categorized the influencing factors of cyberbullying among college students into five sublevels, i.e., background, Internet use and social network habits, personality, emotion, and literacy related to digital citizenship. Then a questionnaire survey was applied to 947 Chinese college students. The results show that cyberbullying among Chinese college students are generally at a low level. There are many factors influence cyberbullying. Specifically, at the personal background level, gender has a significant impact on cyberbullying and being cyberbullied. In terms of personal Internet use and social network habits, students’ average daily online time has no significant correlation with cyberbullying and being cyberbullied; however, the proportion of online non-learning time has a significantly positive correlation with cyberbullying, and the proportion of online learning/work time has a significant impact on being cyberbullied. At the personality level, the Big Five personality traits have varying degrees of correlation with and influence on cyberbullying and being cyberbullied. At the personal emotions level, students’ life satisfaction has a significantly negative correlation with cyberbullying and being cyberbullied while it only has a significant impact on being cyberbullied; the personal stress and empathetic concern aspects of empathy have a significantly positive correlation with cyberbullying and being cyberbullied among female students. At the literacy related to digital citizenship level, students’ understanding of and compliance with Internet etiquette have significantly negative impacts on cyberbullying; the ability to communicate and collaborate online and Internet addiction have significantly positive impacts on cyberbullying and being cyberbullied; the understanding of and compliance with relevant digital laws and regulations have significantly negative correlations with cyberbullying and being cyberbullied. Overall, college students’ digital citizenship level has a significantly negative correlation with cyberbullying but no significant correlation with being cyberbullied. Finally, analysis and suggestions were provided according to these statistical results and the effects of these factors on cyberbullying and being cyberbullied among college students, so as to help solve this problem and provide a new perspective for research in this field.

Introduction

Currently, the Internet has penetrated into all aspects of people’s lives. While providing various conveniences, the Internet has also caused a series of social problems such as spam, Internet addiction, and Internet crime. In recent years, cyberbullying, as a representative of abnormal Internet behaviors, has been prominent in many countries (e.g., the United States, Japan, and Australia), in which countermeasures and preventive measures against cyberbullying have been formulated. Instagram, a well-known social platform, began developing automated cyberbullying filtering tools in 2019. In his book, Ivester (2011) maintains that social media is evolving into an alternative mechanism of communication and contact among people and is continuously in fashion among students, greatly increasing the likelihood of cyberbullying on college campuses ( Washington, 2015 ). This is especially true for Chinese college students. Statistical results show that Internet users aged 10–19 and 20–29 accounted for 14.8 and 19.9% of the whole population in China ( China Internet Network Information Center, 2020 ), and 87.8% of college students love to use social communication applications ( iiMedia Research, 2018 ). Partly because Chinese college students have much free time and are curious about the outside world, which, coupled with the absence of parental supervision, has led to college students being the major Internet users among the adolescent population. However, negative information is becoming more common in digital society. Being inexperienced and immature emotionally and intellectually, without having established the “Three Views” 1 , college students are more inclined to be inadvertently involved in cyberbullying (as a perpetrator or a victim) and exert adverse influences on others and society as a whole.

Under this circumstance, it is necessary to know the current situation of cyberbullying among Chinese college students and reveal potential influencing factors to help cub it effectively. However, the literature survey of the China National Knowledge Infrastructure (CNKI) indicated that as of July 2020, there has been only 13 publications on “cyberbullying” and “influencing factors,” all published after 2015, accounting for 3.8% of all 337 articles with the subject “cyberbullying.” The lack of studies on the influencing factors of cyberbullying makes relevant prevention strategies and containment mechanisms ineffective and impertinent. Additionally, in terms of research objects, most of the previous studies in China have focused on cyberbullying among youth, with only 32 articles on college students and none on influencing factors. In fact, college life is the most critical time before an individual enters society and thus a critical period for the formation and establishment of personality, morals, and the “Three Views.” Being deeply involved in the Internet and digital society, college students should be guided to keep away from cyberbullying. Therefore, understanding the influencing factors of cyberbullying among them and developing targeted prevention strategies are very important for effectively addressing the problem. In this regard, based on discovering the current situation of college student cyberbullying in China, this paper examined its influencing factors from the perspective of individual students to provide suggestions for the intervention and prevention of cyberbullying.

Literature Review and Hypotheses

Literature review.

Literature review showed that the existing studies mainly focused on individual students, families, schools, society, and the environment. Specifically, in terms of individual students, Li (2007) , Kowalski et al. (2012b) , Topcu and Erdur-Baker (2012) and many other investigators revealed that cyberbullying is gender related. Hsu and Wang (2010) found that personality traits are predictive of cyberbullying, and Gibb and Devereux (2014) and Goodboy and Martin (2015) showed that the dark personality theory can describe the common characteristics of cyberbullies: self-righteous, ruthless, and aggressive. From the psychological perspective, Sun and Deng (2016) found that both perpetrators and victims of cyberbullying have more negative emotions; Liu and Xu (2019) found that the psychological factors related to cyberbullying include empathy, narcissism, self-esteem, depression, and anxiety; Gini and Pozzoli (2009) and Renati et al. (2012) found that cyberbullying is associated with an individual’s empathy; cyberbullying perpetrators often lack empathy and have emotional difficulties ( Weaver and Lewis, 2012 ; Barlińska et al., 2013 ). Zhao and Wang (2019) demonstrated that college students’ perception of well-being is closely correlated with their Internet usage, and Li (2007) , You (2013) , Hayton (2017) , and Nurlita et al. (2018) showed that the frequencies of Internet use and social media use have an important impact on cyberbullying.

In terms of family factors, Ybarra and Mitchell (2004) found that cyberbullying is closely related to the relationship between family members; Wang et al. (2012) , Bayraktar et al. (2015) , and Elsaesser et al. (2017) confirmed the connection between cyberbullying behavior and a lack of parental support; and Pillay (2012) and Park et al. (2014) found that cyberbullying is associated with individuals’ family socioeconomic status to some extent. In addition, some studies revealed that parental supervision is also a factor affecting cyberbullying ( Ybarra and Mitchell, 2004 ; Chen and Astor, 2012 ; Kowalski et al., 2012a ; Low and Espelage, 2013 ).

Regarding school factors, Bevilacqua et al. (2017) showed that the degree of cyberbullying varies with school type and quality, and organizational/management factors within a school affect students’ behavior; Guarini et al. (2012) found that students’ negative relationship with teachers and low recognition of the school are risk factors for cyberbullying; and Calvete et al. (2010) and Souza et al. (2018) found that cyberbullying is related to school atmosphere and environment. Moreover, school culture ( Monks et al., 2016 ), safety ( Bottino et al., 2015 ) and regulatory measures ( Song, 2015 ), sense of belonging ( Baldry et al., 2015 ; Chen et al., 2016 ), and education and training on mental health and cybersecurity ( Gao, 2018 ; Liang, 2019 ) are also important factors affecting cyberbullying.

With respect to social and environmental factors, Huang and Chou (2010) argued that cyberbullying behaviors, in various countries, are highly dependent on the environment and are affected by the education system, school environment, cultural norms, and interpersonal relationships. Markward et al. (2001) found that various factors, such as herd mentality, traditional bullying influence, and cultural background differences, affect cyberbullying behavior. In addition, workplace stress ( Vranjes et al., 2017 ) and peer factors ( Liu and Xu, 2019 ) are also related to the risk of cyberbullying among youth, which is also affected by the characteristics of the Internet ( Kiesler et al., 1985 ; Holland, 2012 ).

In recent years, digital citizenship education has gradually attracted widespread attention from scholars around the world. With the aim of cultivating qualified digital citizens in the information age, digital citizenship education requires digital citizens to acquire global awareness, legal awareness as well as digital citizenship awareness so that technology is used in a safe, responsible, and ethical way ( Yang et al., 2016 ). However, the rise and spread of cyberbullying are inextricably linked to each digital citizen: current Internet users are mostly digital natives who have acquired the ability to use information technology but still lack the corresponding technical ethics and responsibilities. In other words, the occurrence of many cyberbullying incidents is the outcome of weak cyber legal and moral awareness among these digital natives. That’s exactly the core of digital citizenship education ( Ivester, 2011 ; Zheng et al., 2020 ). Therefore, while providing a new perspective for the study of cyberbullying, digital citizenship education is an important means to control cyberbullying ( Lin, 2017 ; Zheng et al., 2020 ). In this regard, digital citizenship, in conjunction with the relevant digital citizenship education content were investigated in this study to conduct an in-depth examination on the influencing factors of cyberbullying at the personal level.

The above literature review and analysis categorizes the influencing factors of cyberbullying into four levels: (1) Personal level, including gender, age, personality traits, well-being, empathy, length or frequency of Internet uses, social behavior type, and digital citizenship; (2) Family level, including relationship between family members, parental support, family socioeconomic status, and parental supervision; (3) School level, including school type and teaching quality, school management, teacher-student relationship, school climate and environment, school culture, school safety and supervision, and education and training on mental health and Internet security; (4) Social and environmental level, including national education system, cultural norms, community influence (herd mentality), cultural differences, interpersonal (peer) relationship, work pressure, and Internet characteristics.

Among the above-described influencing factors, those at students’ personal level have a direct impact on students’ cyberbullying behavior, and are the basis for investigating and analyzing the influencing factors of cyberbullying at other levels. So it sounds reasonable to start from the perspective of individual students. Nevertheless, previous studies have focused on students’ personal variables (e.g., gender, age or grade, and personality traits) and Internet usage (e.g., hours online and frequency per day), without considering students’ literacy related to digital citizenship. Therefore, in this study, personal influencing factors of cyberbullying among college students were categorized into five sublevels, i.e., (1) Background (including gender, age, and time to start using the Internet), (2) Internet use and social network habits (including average daily time online, the proportion of online learning/non-learning time, the number of online social communities joined, and social behavior type), (3) Personality [including five personality traits, i.e., openness, neuroticism, extroversion, agreeableness, and conscientiousness ( Howard et al., 1996 )], (4) Emotion (including subjective well-being and empathy), and (5) Literacy related to digital citizenship [including digital identity and dignity, digital citizenship awareness and accountability, the understanding of and compliance with Internet etiquette, digital communication and collaboration capabilities, degree of Internet addiction, and the understanding of and compliance with relevant laws and regulations ( Ribble, 2015 ; Zheng et al., 2020 )].

In order to explore the impact of personal factors on cyberbullying, this study inspected these variables one by one, as illustrated in the following hypotheses:

Hypothesis 1: The degree of cyberbullying among Chinese college students is affected by students’ personal background. Specifically, college students of different genders and with different ages to start using the Internet have significantly different scores regarding the degree of cyberbullying. This hypothesis corresponds to exploring the influence of individual background (sublevel 1) on cyberbullying.

Hypothesis 2: The degree of cyberbullying among Chinese college students is affected by students’ use of the Internet and social network habits. Specifically, cyberbullying among college students has a significantly positive correlation with students’ length of time online and the proportion of online non-learning time, and students who show different social network habits differ significantly regarding cyberbullying. This hypothesis corresponds to exploring the influence of individual Internet use and social network habits (sublevel 2) on cyberbullying.

Hypothesis 3: The degree of cyberbullying among Chinese college students is affected by students’ personality traits. Specifically, the degree of cyberbullying has a significantly positive correlation with neuroticism and openness but a significantly negative correlation with extroversion, agreeableness, and conscientiousness. This hypothesis corresponds to exploring the influence of individual personality (sublevel 3) on cyberbullying.

Hypothesis 4: The degree of cyberbullying among Chinese college students is affected by students’ emotions. Specifically, the degree of cyberbullying has a significantly negative correlation with their life satisfaction and empathy. This hypothesis corresponds to exploring the influence of individual emotion (sublevel 4) on cyberbullying.

Hypothesis 5: The degree of cyberbullying among Chinese college students is affected by students’ level of digital citizenship and has a significantly positive correlation with their degree of Internet addiction and a significantly negative correlation with their digital identity and dignity, digital citizenship awareness and accountability, understanding of and compliance with Internet etiquette, digital communication and collaboration skills, and understanding of and compliance with relevant laws and regulations. This hypothesis corresponds to exploring the influence of individual literacy related to digital citizenship (sublevel 5) on cyberbullying.

Research Design and Implementation

Research subjects and process.

In this study, through random sampling, college students and graduate students of different cities in China took part in this online survey anonymously. Specifically, a text message and a questionnaire link were first sent to the students of South China Normal University randomly via social communication software (e.g., WeChat groups, QQ groups), then they were asked to forward the message to their classmates or ex-classmates (e.g., their high school classmates but now learning in different universities). Gradually the survey was spread out in a non-linear way. Each student was asked to provide responses to the survey within a specified time. Since ethical review and approval is not required for the study on human participants in accordance with the local legislation and institutional requirements of China, an instruction about the purpose of this survey and how the data will be used later was provided at the beginning of the questionnaire, so that the participants had a total understanding of the survey. Eventually a total of 1,188 online questionnaires were collected, of which 947 were valid, for an effective rate of 79.7%.

Questionnaire Design

The questionnaire consisted of five parts:

(1) Questions regarding students’ personal background, Internet use and social network habits, including students’ gender, age, time to start using the Internet, average daily time online, proportion of online learning/non-learning time, number of online social communities joined, and types of social behavior, in a total of seven items. In China, students mainly use popular social networking platforms such as Sina Microblog, Tencent Microblog, QQ Groups, WeChat Groups, Tianya social community, Zhihu social community, and the like. Of course, some of them may use Facebook, Instagram, Twitter or similar platforms. They will all be considered by default when it comes to statistical analysis of one’s online social networking experience. This instruction was also provided in the questionnaire to make students clearly understand.

(2) A personality questionnaire, i.e., The Big Five Personality Test, compiled by Howard et al. (1996) and used to measure the personality inclination of college students, in a total of 25 items. This questionnaire has been widely used in many studies, with high reliability and validity [0.736 < Cronbach’s α < 0.904 and KMO = 0.806 ( Hee, 2014 )].

(3) Emotion questionnaires to analyze subjective well-being and empathy, measured, respectively, with the Life Satisfaction Scale developed by Diener et al. (1985) and the Interpersonal Reactivity Index scale compiled by Davis (1980) . Both scales have been tested and have good reliability and validity [Cronbach’s α = 0.86 and KMO = 0.84 for the Life Satisfaction Scale ( Silva et al., 2015 ) and Cronbach’s α = 0.75 and KMO = 0.833 for the Interpersonal Reactivity Index Scale ( Zhang et al., 2010 )]. There are totally 27 items in this part.

(4) A digital citizenship questionnaire that measures, using 35 questions answered with a five-point Likert scale, digital identity and dignity, digital citizenship awareness and accountability, the understanding of and compliance with Internet etiquette, digital communication and collaboration capabilities, degree of Internet addiction, and the understanding of and compliance with relevant laws and regulations. Among them, the Internet Addiction Scale was derived from the simplified version of Young’s Internet Addiction Test with high reliability and validity [Cronbach’s α = 0.848 and KMO = 0.924 ( Pawlikowski et al., 2013 )], the scales for the rest variables were modified from or developed based on, respectively, the self-esteem scale for the assessment of adolescents’ self-worth and self-acceptance by Rosenberg (1965) , the digital citizenship scale ( Al-Zahrani, 2015 ), the monograph on digital citizenship education by Ribble (2015) and the content decomposition of digital citizenship by Zheng et al. (2020) . The whole questionnaire in this part was tested in this study and found to have good reliability and validity (Cronbach’s α = 0.789 and KMO = 0.671).

(5) A cyberbullying questionnaire derived from Topcu and Erdur-Baker’s (2010) Cyberbullying Scale that measures the degree to which college students act as perpetrators or victims of cyberbullying. The questionnaire uses 14 items for 14 cyberbullying behaviors, with another 14 for being cyberbullied behaviors. So totally there are 28 items, with high reliability and validity [Cronbach’s α = 0.818 and KMO = 0.873 ( Murwani, 2019 )]. In order to get a better understanding of how personal factors have influence on cyberbullying among college students, the questionnaire limits cyberbullying experience (commit or suffer) to be within the recent one or 2 years. In other words, students will be asked if they have had these experiences (14 cyberbullying behaviors and 14 being cyberbullied behaviors) recently.

Descriptive Statistics

Figure 1 shows the geographical distribution of the respondents. It’s clear that the participants were mostly from big and modern cities of China, such as Guangzhou, Beijing, Zhengzhou, and Shenzhen, where Internet access is easier and faster, and social network application is more popular as well.

Figure 1. Geographical distribution of the respondents.

The respondents’ demographic information, Internet use and social network habits are shown in Table 1 . They were young people with an average age of 20.71 (SD = 2.234). Two-thirds of them were female, indicating that in China girls showed more willingness to help others academically than boys. Over one-half of the respondents (53.9%) started their online experience prior to middle school; on average, 45.2% of the students spent 3–6 h online daily, and one-third of the students spent over 6 h online daily. College students spent an average of 66.63% of time online on social networks and doing other activities unrelated to learning. When using social networks, 54.1% of the students joined at least three online communities while 65.3% did not participate in any online discussions.

Table 1. Statistics for college students’ background information, Internet use and social network habits.

Current Situation of Cyberbullying Among College Students

According to Topcu and Erdur-Baker’s (2010) Cyberbullying Scale, the total score ranges from 14 to 56 points. The higher the score is, the higher the level of cyberbullying or being cyberbullied. As shown in Table 2 , overall, the average cyberbullying score for the 947 college students was 17.14, indicating a low cyberbullying level; the average score for being a victim of cyberbullying was 19.93, which is low but higher than that for cyberbullying. Among the 14 cyberbullying behaviors, “Making fun of comments in online forums” appeared most frequently in both situations ( M = 2.20 and SD = 1.319 for cyberbullying, and M = 1.88 and SD = 1.201 for being cyberbullied), while “Excluding others by blocking or moving their comments” ( M = 1.87 and SD = 1.077) and “Stealing email access (usernames and passwords) and blocking true owner’s access” ( M = 1.84 and SD = 0.999) ranked second in frequently appeared forms of cyberbullying and being cyberbullied, respectively.

Table 2. Statistics for cyberbullying among college students.

According to Brack and Caltabiano (2014) , when committing (suffering) any of the 14 behaviors two or more times, an individual can be deemed as a cyberbullying perpetrator (victim). Those with a dual identity of cyberbullying perpetrator and victim must meet the standards for a cyberbullying perpetrator and victim simultaneously while those who are deemed as non-participants either never committed or experienced any cyberbullying or experienced one incident, at most, of cyberbullying or being cyberbullied. According to these criteria, the proportion of college students who are cyberbullying victims (58.6%) is a bit higher than that of students who are cyberbullying perpetrators (51.2%), and more than 40% of them have a dual identity as both a victim and perpetrator (41.6%); approximately one-third of the students have never experienced cyberbullying (31.8%). Though results show high percentages of cyberbullying and being cyberbullied (over 50%), the most frequent form of both cyberbullying and being cyberbullied is making fun of comments on forums (it’s very common in this era), and the average scores are 17.14 and 19.93 (out of 56), respectively, with SD less than 2. Therefore, it is believed that cyberbullying is generally at a relatively low level among Chinese college students, so is being cyberbullied.

Influencing Factors of Cyberbullying Among College Students

Effect of personal background on cyberbullying.

Gender differences in cyberbullying were examined through the two independent samples non-parametric test. As shown in Table 3 , the progressive significance values are lower than 0.05, indicating that gender differences in cyberbullying is significant. The scores for male students are significantly higher than those for female students, indicating that male students are more likely to cyberbully others or be cyberbullied by others than are female students.

Table 3. Significance tests for gender differences in cyberbullying.

Time to start using the Internet

The relationship between the time to start using the Internet and cyberbullying was examined through the two independent samples non-parametric test. As shown in Table 4 , the progressive significance values are lower than 0.05, indicating that students with different ages to start using the Internet differ significantly regarding cyberbullying.

Table 4. Significance tests for time to start using the Internet in cyberbullying.

Effect of Internet Use and Social Network Habits on Cyberbullying

Internet use.

The correlation between the degree of cyberbullying and daily average time online or daily average non-learning time online was analyzed using the Spearman correlation method. As shown in Table 5 , daily average time online is not significantly correlated to cyberbullying while daily non-learning time online is significantly positively correlated with the degree of cyberbullying but is not significantly correlated with the degree of being cyberbullied.

Table 5. Correlation between Internet use and cyberbullying.

Social network behavior

The effect of social behavior type on the degrees of cyberbullying and being cyberbullied was analyzed through variance analysis. As shown in Table 6 , the significance values are all lower than 0.05, indicating that different social behaviors have significant effects on cyberbullying among college students.

Table 6. Variance analysis results for the effect of social behavior type on cyberbullying.

Effect of Personality Traits on Cyberbullying

The relationship between the personality traits of college students and cyberbullying behavior was examined through the Big Five Personality Test and Spearman correlation analysis. As shown in Table 7 , the degree of cyberbullying is significantly positively correlated with openness and significantly negatively correlated with neuroticism, agreeableness and conscientiousness. The degree of being cyberbullied is significantly positively correlated with openness, and significantly negatively correlated with neuroticism and conscientiousness.

Table 7. Correlation between Big Five personality traits and cyberbullying.

Effect of Emotions on Cyberbullying

Life satisfaction.

The results of the Spearman correlation between life satisfaction and cyberbullying/being cyberbullied are shown in Table 8 , indicating that students’ life satisfaction is negatively correlated with the degree of cyberbullying as well as with the degree of being cyberbullied.

Table 8. Correlation between life satisfaction and cyberbullying.

Given the gender differences in empathy, the samples were grouped based on two genders, and Spearman correlation between empathy and cyberbullying was conducted for the two groups, respectively. As shown in Table 9 , the correlation between each of the empathy variables and cyberbullying (or being cyberbullied) is non-significant in the male student group while the personal distress and empathetic concern variables of empathy are significantly positively correlated with both cyberbullying and being cyberbullied in the female student group.

Table 9. Correlation between empathy and cyberbullying.

Effect of Digital Citizenship on Cyberbullying

The effect of digital citizenship on cyberbullying among college students was examined through the Spearman correlation of cyberbullying with students’ digital identity and dignity, digital citizenship awareness and accountability, understanding of and compliance with Internet etiquette, digital communication and collaboration capabilities, and understanding of and compliance with relevant laws and regulations. As shown in Table 10 , the average scores for all variables related to college students’ digital citizenship (except Internet addiction) are higher than 10; that for students’ understanding of and compliance with relevant laws and regulations is the highest, and that for students’ digital communication and collaboration capabilities is the lowest. The correlation analysis results showed that the degrees of cyberbullying and being cyberbullied are significantly positively correlated with students’ digital communication and collaboration capabilities, and are significantly negatively correlated with students’ understanding of and compliance with relevant laws and regulations; whereas only the degree of cyberbullying is significantly negatively correlated with students’ understanding of and compliance with Internet etiquette. In general, students’ level of digital citizenship is significantly negatively correlated with the degree of cyberbullying but is not significantly correlated with the degree of being cyberbullied.

Table 10. Statistics for students’ digital citizenship and correlations between students’ digital citizenship and cyberbullying.

In order to reveal the relationship between Internet addiction and cyberbullying, the Internet addiction status of Chinese college students was first analyzed, then followed by the correlation between Internet addiction and cyberbullying/being cyberbullied through Pearson correlation analysis. For the Internet Addiction Scale, the higher the score is, the higher the degree of Internet addiction; a score above 40 indicates an Internet addiction. As shown in Tables 11 , 12 , 19.3% of the students are addicted to the Internet, and the students’ Internet addiction is significantly positively correlated with the degree of cyberbullying or being cyberbullied, indicating that the higher the degree of a student’s Internet addiction, the more likely that student is to commit cyberbullying or be cyberbullied.

Table 11. Internet addiction among college students.

Table 12. Correlation between Internet addiction and cyberbullying among college students.

Multivariate Regression Analysis of Influencing Factors of Cyberbullying

To further examine the joint effects of these personal factors on cyberbullying among college students, multivariate regression analyses were conducted using the above variables as independent variables and the degrees of cyberbullying and being cyberbullied as dependent variables; the samples were grouped based on social behavior type, with the socially active group as the reference group and students who do not participate in discussions (accounting for 65.3% of the total sample) as an example in the analysis.

As shown in Table 13 , after excluding several non-significant variables based on the F -test, nine predictors remained in the regression equation for cyberbullying factors, each having a tolerance greater than 0.4 and a VIF value below 5, indicating that these nine predictors retained in the regression equation do not have a multicollinearity problem. The significance of the F value (sig.) is lower than 0.001, indicating that these predictors have a significant linear relationship with the degree of cyberbullying. Specifically, at the personal background level, gender has a significant impact on the degree of cyberbullying. At the Internet use and social network habits level, social behavior type and the number of online communities joined have significant impacts on the degree of cyberbullying. At the personality trait level, only conscientiousness has a significantly positive impact on the degree of cyberbullying, while other traits were eliminated in the stepwise linear regression, indicating that other aspects of the Big Five personality traits have no significant linear relationships with the degree of cyberbullying. At the digital citizenship level, Internet addiction, digital communication and collaboration capabilities, and digital citizenship awareness and accountability have significantly positive impacts on the degree of cyberbullying, while students’ understanding of and compliance with Internet etiquette has a significantly negative impact on the degree of cyberbullying.

Table 13. Results of the multivariate regression analysis of factors influencing the degree of cyberbullying in students who do not participate in online discussions.

In the stepwise multivariate regression equation for factors influencing the degree of being cyberbullied, ten predictors remained in the equation, each having a tolerance greater than 0.4 and a VIF value below 5, showing no multicollinearity problem between the variables. The significance of the F value (sig.) is lower than 0.001, indicating that these predictors have a significant linear relationship with the degree of being cyberbullied. As shown in Table 14 , at the personal background level, gender has a significant impact on the degree of being cyberbullied. At the Internet use and social network habits level, the number of online communities joined and online learning/work time has significant impacts on the degree of being cyberbullied. At the emotion level, life satisfaction has a significantly negative impact on the degree of being cyberbullied. At the personality level, conscientiousness has a significantly positive impact on the degree of being cyberbullied. At the digital citizenship level, the degree of Internet addiction, digital communication and collaboration capabilities, and digital identity and dignity have significantly positive impacts on the degree of being cyberbullied.

Table 14. Results of the multivariate regression analysis of factors influencing the degree of being cyberbullied in students who do not participate in online discussions.

This study randomly selected 947 college students in China as survey subjects to investigate the current situation of cyberbullying and conducted an in-depth analysis on the impact of students’ personal background, Internet use and social network habits, personality traits, emotions and literacy related to digital citizenship on the degrees of cyberbullying and being cyberbullied. Further analysis and discussions are presented as follows.

Effect of Student’s Personal Background on Cyberbullying Among College Students

Regarding gender, the male students’ total scores for cyberbullying and being cyberbullied were significantly higher than those for the female students, indicating that males are more likely to cyberbully others or be cyberbullied by others than are females, which is consistent with the results of some previous studies ( Calvete et al., 2010 ; Huang and Chou, 2010 ; Ozden and Icellioglu, 2014 ; Safaria, 2016 ; Beyazit et al., 2017 ) but contrary to those of others ( Smith et al., 2008 ; Ortega et al., 2009 ; Sourander et al., 2010 ; Giménez-Gualdo et al., 2015 ), likely because in different countries, regions or schools, the understanding and identification of cyberbullying differ, and there are many measurement scales in this field, in which certain behaviors deemed as cyberbullying are controversial. On the other hand, the Internet use awareness and online behavior of different survey subjects vary and are closely related to their education and experience from childhood onward. In addition, the methods for cyberbullying commonly used by male and female students also differ ( Slonje and Smith, 2008 ; Wong et al., 2014 ). Therefore, there are three different conclusions regarding the effect of gender on cyberbullying: more males commit cyberbullying, more females commit cyberbullying, and both genders commit cyberbullying equally ( Hinduja and Patchin, 2008 ; Guarini et al., 2012 ; Pillay, 2012 ; Gibb and Devereux, 2014 ). Therefore, this remains an open question. In regard to the participants in this study, male students had stronger personalities and were more volatile than female students and thus more inclined to have conflicts with others, leading to cyberbullying ( Zhu et al., 2016 ).

In addition, time to start using the Internet is significantly correlated with students’ cyberbullying or being cyberbullied, but the two showed no regression relationship, which is likely related to the students’ Internet awareness, skills and experience. Early exposure to the Internet allows students to have stronger Internet use awareness, more Internet skills and richer Internet experience, making these students more adept to cyberspace and prone to bully newbies intentionally or unintentionally. On the other hand, the participation of college students have been growing in various online forums and communities, which, in the early stage, were relatively open and laden with all kinds of information for which effective supervision and reporting mechanisms lacked; therefore, the longer a student has had access to the Internet (i.e., the earlier the time to start using the Internet), the more cyberbullying the student would have suffered.

These results confirm Hypothesis 1 listed in section “Hypotheses,” suggesting that in cyberbullying intervention and governance processes, it is necessary to pay close attention to the social behavior of male students, especially those with an early age to start using the Internet.

Effect of Students’ Internet Use and Social Network Habits on Cyberbullying

Regarding average daily time online, though daily time online is not correlated with cyberbullying, daily non-learning time online is significantly positively correlated (but no regression relationship) with the degree of cyberbullying, and the proportion of learning/work time online has a significant regression relationship with the degree of being cyberbullied. In other words, the longer the daily non-learning time a student spends online, the more likely he/she is to become a perpetrator of cyberbullying; the longer the daily learning/work time a student spends online, the more likely he/she is to become a cyberbullying victim. In previous studies, time online was not divided into learning and non-learning hours, but cyberbullying usually occurs in non-learning situations, such as social interactions, games, and entertainment; therefore, the conclusions of this study can be considered consistent with those of previous studies ( Hinduja and Patchin, 2008 ; Sticca et al., 2013 ; Zhu et al., 2016 ). This result indicates that students with different purposes and uses for the Internet have different effects on others. Lingering on social network and leisure sites makes these students more susceptible to disinformation or misinformation, prompting them to use offensive and threatening language, send tasteless pictures that violate others’ privacy, or place blame on teammates when playing online games, thereby cyberbullying others.

In terms of social behavior, different types of online behavior are significantly correlated with cyberbullying or being cyberbullied. Regarding average cyberbullying scores, students who are self-expressive and participate in discussions are more inclined to cyberbully others. Students with these two behaviors belong to active social network types and are prone to voice their views and follow suit when participating in debates; when questioned or refuted or when questioning or debating others, these students are liable to have conflict with others and even engage in cyber-stalking and violate the privacy of others, thereby cyberbullying others. Regarding average scores for being cyberbullied, students who are self-expressive had significantly higher scores than those of students with other behaviors, indicating that those who like to voice their opinions and ideas online are more likely to be cyberbullied, especially when their opinions or views are not accepted by others.

These results mostly confirm Hypothesis 2, suggesting that in the cyberbullying intervention and governance processes, it is necessary to strictly control the non-learning/work hours of college students and treat those with different social behaviors differently, so that targeted measures can be taken to prevent cyberbullying.

Effect of College Students’ Personality on Cyberbullying

First, the personality trait “openness” is significantly positively correlated with cyberbullying and being cyberbullied, i.e., college students with a high level of openness are more likely to cyberbully others or be cyberbullied, which is consistent ( Hsu and Wang, 2010 ; You, 2013 ; Peluchette et al., 2015 ) or partially consistent ( Celik et al., 2012 ) with the results reported in other studies, indicating that these students are curious about the outside world, fond of trying new things and thus more prone to be involved in Internet events or comment on others’ opinions, leading to online conflicts. Moreover, students with a high degree of openness have more Internet interactions on a wider range of topics and thus are more prone to be exposed to misinformation or disinformation while fully exposing their own information on the Internet, making them more susceptible to cyberbullying.

Second, neuroticism and conscientiousness are significantly negatively correlated with students’ cyberbullying and being cyberbullied, i.e., college students with strong neuroticism and those who are conscientious are less likely to cyberbully others or be cyberbullied, which is consistent ( Festl and Quandt, 2013 ; You, 2013 ) or partially consistent ( Celik et al., 2012 ) with the results of other studies, indicating that college students who can more effectively balance emotions, such as anxiety and hostility, maintain emotional stability and are more organized, with a greater sense of responsibility and self-control, are less likely to exhibit cyberbullying behaviors and be cyberbullied.

Third, agreeableness is significantly negatively correlated with cyberbullying, i.e., college students with a high level of agreeableness are less likely to cyberbully others, which is consistent with the result of a previous study ( Celik et al., 2012 ). Students with a high level of agreeableness give priority to others, get along with others well and interact with others more harmoniously and thus are popular among others; they are often friendly and considerate and rarely bully others online. However, agreeableness is not significantly correlated with being cyberbullied, which is inconsistent with the findings of other studies ( Celik et al., 2012 ; You, 2013 ; Semerci, 2017 ), likely because students with a high level of agreeableness are always ready to help others and friendly to others; therefore, they are less likely to become a target of bullying by others.

These results partly confirm Hypothesis 3, suggesting that in cyberbullying intervention and governance processes, it is necessary to first determine a student’s personality traits and propose specific measures for college students with different personalities, and if conditions permit, big data and data mining techniques can be employed to determine their personality traits and predict cyberbullying behavior more accurately.

Effect of Students’ Emotions on Cyberbullying

Students’ life satisfaction is significantly negatively correlated with cyberbullying and being cyberbullied and has a significant impact on being cyberbullied, indicating that the higher the level of students’ life satisfaction, the less likely the students will bully others or be bullied, which is consistent with the results of a previous study ( Zhu et al., 2016 ) but different from those of another study ( Pillay, 2012 ); this inconsistency is likely due to the differences between college students in China and other countries when perceiving happiness and the aspects different assessment scales focusing on.

In terms of empathy, personal stress, and empathic concern are significantly positively correlated with cyberbullying and being cyberbullied among female students; however, this correlation is absent among male students, indicating that gender plays a mediating role in the effect of empathy on cyberbullying, which is consistent with the results of some early studies ( Topcu and Erdur-Baker, 2012 ; Baldry et al., 2015 ; Del Rey et al., 2016 ) but contrary to those of other studies ( Renati et al., 2012 ; Brewer and Kerslake, 2015 ; Peterson and Densley, 2017 ). These inconsistent results are likely due to the differences in the active areas of male and female brains regarding displaying empathy ( Schulte-Rüther et al., 2008 ); the emotional awareness of females is stronger, making them more inclined to sympathize and emphasize with others’ stress and perceive and understand others by taking the position of others, ultimately resulting in “being involved too deeply to be able to disengage” and thus being more susceptible to being cyberbullied. They may also turn empathy into vengeance and condemn those who they consider perpetrators through inappropriate ways, such as breeching privacy, verbal abuse and insults, turning a self-righteous act into cyberbullying.

These results mostly confirm Hypothesis 4, suggesting that in cyberbullying intervention and governance processes, it is necessary to pay attention to students’ life satisfaction as well as the emotional stability of female students and integrate Internet supervision mechanism to dynamically display students’ emotional data so that cyberbullying behaviors can be accurately monitored and prevented.

Effect of College Students’ Literacy Related to Digital Citizenship on Cyberbullying

In the first place, students’ understanding of and compliance with Internet etiquette has a significantly negative impact on cyberbullying, indicating that college students’ understanding and recognition of digital ethics, such as Internet etiquette and technical etiquette, actively practicing positive ethics and codes of conduct in the digital space, and regulating their behaviors in digital society through etiquette in real society can allow the vast majority of people to enjoy the convenience and joy brought by digital technology and effectively reduce the probability of cyberbullying. Therefore, it is advisable to fully acknowledge the advantages of school, family and community education, improve college students’ awareness of Internet etiquette, expand the Internet etiquette knowledge base, and cultivate relevant operational skills and norms in all life aspects through supplementation with various lifelong education models, coupled with related online and offline promotion to effectively improve college students’ understanding of and compliance with Internet etiquette, so as to effectively prevent cyberbullying.

In the second place, college students’ digital communication and collaboration capabilities have a significantly positive impact on cyberbullying and being cyberbullied. Cyberbullying mainly manifests as verbal abuse with insulting and offensive language, or privacy disclosures. The results showed that college students who are more able to skillfully select appropriate means of communication and collaboration with others online are more adept at mastering a variety of communication means and skills; once their emotions are out of control, they are prone to voice some inappropriate opinions or disclose the privacy of others, thus resulting in cyberbullying. On the other hand, college students with digital communication and collaboration capabilities are more likely to join more online communities, have richer online social networks or collaboration experience and spend longer amounts of time online, increasing their likelihood of being cyberbullied. Therefore, it is necessary to supervise and control the time and space of communication and collaboration; in particular, schools and families should pay special attention to those students with strong digital communication and collaboration capabilities, and when necessary, administrative and technical means should be used to strictly manage their social networks and collaborations to prevent cyberbullying incidents.

In the third place, college students’ degree of Internet addiction has a significantly positive impact on cyberbullying and being cyberbullied, indicating that students who are more addicted to the Internet are more dependent on the Internet, resulting in higher probabilities of cyberbullying others and being cyberbullied, which is consistent with the results of earlier studies ( Floros et al., 2013 ; Chang et al., 2015 ; Hou, 2017 ). College students are not fully mature mentally, are profoundly affected by emotions and have not yet formed the “Three Views”; when lingering online for too long, they are vulnerable to mental, emotional, and moral erosion through misinformation and disinformation on the Internet and thus develop negative behaviors, intentionally or unintentionally cyberbullying others or being cyberbullied by others. Therefore, it is necessary to pay attention to their digital health and wellness; in schools and families, when necessary, administrative and technical means should be utilized to strictly monitor and control their online time, establish an early warning mechanism for excessive Internet use and take various anti-addiction measures to prevent Internet addiction, encouraging them to find a balance between online and offline life.

In the fourth place, college students’ understanding of and compliance with relevant digital laws and regulations are significantly negatively correlated with cyberbullying and being cyberbullied, indicating that the understanding of and compliance with laws and policies on technology use, especially rules related to Internet ethics, digital rights and responsibilities in the form of legal regulations (e.g., copyright protection for intellectual property), are particularly important for college students’ online behavior. These laws and regulations restrict and regulate the online behaviors, allowing them to clearly know which behaviors are illegal in digital society so that they can strictly abide by them, which helps to significantly reduce the probability of cyberbullying and being cyberbullied. Therefore, it is necessary to strengthen college students’ knowledge and understanding of relevant digital laws and regulations through education at schools, in families and in the community, guiding them to use information technology legally and regulating their words and actions online to avoid cyberbullying and being cyberbullied.

In general, the level of digital citizenship is significantly negatively correlated with the degree of cyberbullying but is not significantly correlated with the degree of being cyberbullied, indicating that improving college students’ digital citizenship level can help significantly reduce their likelihood of cyberbullying others, which mostly confirms Hypothesis 5. Digital citizenship is about the values, necessary qualities, key abilities, and behavior habits for using technology safely, legally, and ethically ( Hao, 2014 ; Zheng et al., 2020 ). Improving college students’ literacy related to digital citizenship will definitely lead to their mastery of knowing how to use technology legally and ethically in daily learning and life, so that the probability of cyberbullying and being cyberbullied among college students can be reduced, and the harm to individuals’ body and mind as well as to society can be avoided, which will ultimately purify cyberspace to a certain extent and prompt the formation of a healthy cyber civilization. Education departments and schools should emphasize and strengthen college students’ digital citizenship education to enhance their digital citizenship in all aspects, thereby ensuring better survival and development in the digital world.

While bringing convenience to people’s interactions, the Internet also causes an obscuration of values and a deficiency in subjectivity ( Hao, 2014 ). It has been well established that cyberbullying has become one of the increasingly serious social problems in the Internet era. Preventing cyberbullying not only relies on means that emphasize “blocking” approaches, such as traditional Internet monitoring, regulations, and legislation, but also requires the adoption of “dredging” approaches to guide youth to correct online behaviors and improve their digital citizenship level, which is also one of the main objectives of digital citizenship education ( Lin, 2017 ; Zheng et al., 2020 ). Incorporated with digital citizenship, this study conducted a questionnaire survey to assess the current situation of cyberbullying among Chinese college students and examined the effect of students’ personal background, Internet use and social network habits, personality traits, emotions, and digital citizenship on cyberbullying from the perspective of individual students. The results showed that cyberbullying among college students is generally at a low level but still requires attention. At the personal background level, gender has a significant impact on college students’ cyberbullying and being cyberbullied, and the time to start using the Internet is significantly correlated to cyberbullying and being cyberbullied but has no significant impact on them. At the personal Internet use and social network habits level, the students’ average daily time online is not significantly correlated with cyberbullying and being cyberbullied; however, the proportion of online non-learning time is significantly positively correlated with cyberbullying, and the proportion of online learning/work time has a significant influence on students’ being cyberbullied. At the personality trait level, different Big Five personality traits have different correlations with and impacts on cyberbullying and being cyberbullied: openness is significantly positively correlated with cyberbullying and being cyberbullied; neuroticism and conscientiousness are significantly negatively correlated with cyberbullying and being cyberbullied; and agreeableness is significantly negatively correlated with cyberbullying. At the personal emotion level, life satisfaction is significantly negatively correlated with cyberbullying and being cyberbullied and has a significant impact on being cyberbullied; the personal stress and empathetic concern aspects of empathy are significantly positively correlated with cyberbullying and being cyberbullied among female students. At the personal digital citizenship level, students’ understanding of and compliance with Internet etiquette has a significant negative impact on cyberbullying, and digital communication and collaboration capabilities and Internet addiction have significantly positive impacts on cyberbullying and being cyberbullied; furthermore, their understanding of and compliance with digital laws and regulations is significantly negatively correlated with cyberbullying and being cyberbullied. Overall, college students’ digital citizenship level is significantly negatively correlated with cyberbullying but is not significantly correlated with being cyberbullied.

In this study, an attempt was made to explore the influencing factors of cyberbullying among college students, not only enriching the theory and practice of cyberbullying among students but also providing a new perspective for research in this field. Limited by several conditions, this paper only surveyed a small group of college students from modern cities in China. In a follow-up study, the sample size should be expanded as much as possible to provide more rational and reliable data support for drawing conclusions with a higher reference value. Furthermore, the effect of other levels such as the family, school, society, and the environment on cyberbullying should be taken into account so that comprehensive measures and governance processes can be developed to effectively curb cyberbullying among college students.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author Contributions

JZ: literature search, methodology, questionnaire survey, data analysis, and writing–review and editing. YZ: supervision, conceptualization, writing–original draft preparation, and review and editing. XH: literature search, questionnaire survey, and data analysis. DM and JG: literature search and questionnaire survey. ML: questionnaire survey and data analysis. JH: methodology and writing–revision and editing. All authors have read and agreed to the published version of the manuscript.

This research was funded by the Department of Policy and Regulation of the Ministry of Education, grant number Jybzfs2018115, and the National University Student Innovation and Entrepreneurship Training Program, grant number 201910574042.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

• ^ View of world: The fundamental cognitive orientation of an individual or society encompassing the whole of the individual’s or society’s knowledge and point of view. View of life: The general and fundamental view of the purpose and meaning of life, the path of life and the way of life formed by people in practice. It determines the goal of people’s practical activities, the direction of life, and also the value orientation of people’s behavior choices and their attitude toward life. View of value: Cognitions, understandings, judgments, or choices made based on people’s certain thinking and senses. That is, a kind of thinking or orientation by which people recognize things and distinguish right from wrong.

Al-Zahrani, A. (2015). Toward digital citizenship: examining factors affecting participation and involvement in the Internet society among higher education students. Int. Educ. Stud. 8, 203–217. doi: 10.5539/ies.v8n12p203

CrossRef Full Text | Google Scholar

Baldry, A. C., Farrington, D. P., and Sorrentino, A. (2015). “Am I at risk of cyberbullying”? A narrative review and conceptual framework for research on risk of cyberbullying and cybervictimization: the risk and needs assessment approach. Aggress. Violent Behav. 23, 36–51. doi: 10.1016/j.avb.2015.05.014

Barlińska, J., Szuster, A., and Winiewski, M. (2013). Cyberbullying among adolescent bystanders: role of the communication medium, form of violence, and empathy. J. Community Appl. Soc. Psychol. 23, 37–51. doi: 10.1002/casp.2137

Bayraktar, F., Machackova, H., Dedkova, L., Cerna, A., and Ševčíková, A. (2015). Cyberbullying: the discriminant factors among cyberbullies, cybervictims, and cyberbully-victims in a czech adolescent sample. J. Interpers. Violence 30, 3192–32816. doi: 10.1177/0886260514555006

PubMed Abstract | CrossRef Full Text | Google Scholar

Bevilacqua, L., Shackleton, N., Hale, D., Allen, E., Bond, L., Christie, D., et al. (2017). The role of family and school-level factors in bullying and cyberbullying: a cross-sectional study. BMC Pediatr. 17:160. doi: 10.1186/s12887-017-0907-8

Beyazit, U., Simsek, S., and Ayhan, A. B. (2017). An examination of the predictive factors of cyberbullying in adolescents. Soc. Behav. Pers. 45, 1511–1522. doi: 10.2224/sbp.6267

Bottino, S. M. B., Bottino, C. M. C., Regina, C. G., Correia, A. V. L., and Ribeiro, W. S. (2015). Cyberbullying and adolescent mental health: systematic review. Cad. Saúde Pública 31, 463–475. doi: 10.1590/0102-311x00036114

Brack, K., and Caltabiano, N. (2014). Cyberbullying and self-esteem in Australian adults. Cyberpsychology 8:7. doi: 10.5817/CP2014-2-7

Brewer, G., and Kerslake, J. (2015). Cyberbullying, self-esteem, empathy and loneliness. Comput. Hum. Behav. 48, 255–260. doi: 10.1016/j.chb.2015.01.073

Calvete, E., Orue, I., Estevez, A., Villardon, L., and Padilla, P. (2010). Cyberbullying in adolescents: modalities and aggressors’ profile. Comput. Hum. Behav. 26, 1128–1135. doi: 10.1016/j.chb.2010.03.017

Celik, S., Atak, H., and Erguzen, A. (2012). The effect of personality on cyberbullying among university students in Turkey. Eurasian J. Educ. Res. 49, 129–150.

Google Scholar

Chang, F. C., Chiu, C. H., Miao, N. F., Chen, P. H., Lee, C. M., Chiang, J. T., et al. (2015). The relationship between parental mediation and Internet addiction among adolescents, and the association with cyberbullying and depression. Compr. Psychiatry 57, 21–28. doi: 10.1016/j.comppsych.2014.11.013

Chen, J. K., and Astor, R. A. (2012). School variables as mediators of personal and family factors on school violence in taiwanese junior high schools. Youth Soc. 44, 175–200. doi: 10.1177/0044118x12448145

Chen, L., Ho, S. S., and Lwin, M. O. (2016). A meta-analysis of factors predicting cyberbullying perpetration and victimization: from the social cognitive and media effects approach. New Media Soc. 19, 1194–1213. doi: 10.1177/1461444816634037

China Internet Network Information Center (2020). The 46th China Statistical Report on Internet Development. Available online at: http://www.cac.gov.cn/2020-04/27/c_1589535470378587.htm (accessed February 3, 2021).

Davis, M. H. (1980). A multidimensional approach to individual differences in empathy. JSAS Cat. Sel. Doc. Psychol. 10:80.

Del Rey, R., Lazuras, L., Casas, J. A., Barkoukis, V., Ortega-Ruiz, R., and Tsorbatzoudis, H. (2016). Does empathy predict (cyber) bullying perpetration, and how do age, gender and nationality affect this relationship? Learn. Individ. Differ. 45, 275–281. doi: 10.1016/j.lindif.2015.11.021

Diener, E., Emmons, R. A., Larsen, R. J., and Griffin, S. (1985). The satisfaction with life scale. J. Pers. Assess. 49, 71–75.

Elsaesser, C., Russell, B., Ohannessian, M. C., and Patton, D. (2017). Parenting in a digital age: a review of parents’ role in preventing adolescent cyberbullying. Aggress. Violent Behav. 35, 62–72. doi: 10.1016/j.avb.2017.06.004

Festl, R., and Quandt, T. (2013). Social relations and cyberbullying: the influence of individual and structural attributes on victimization and perpetration via the Internet. Hum. Commun. Res. 39, 101–126. doi: 10.1111/j.1468-2958.2012.01442.x

Floros, G. D., Siomos, K. E., Fisoun, V., Dafouli, E., and Geroukalis, D. (2013). Adolescent online cyberbullying in greece: the impact of parental online security practices, bonding, and online impulsiveness. J. Sch. Health 83, 445–453. doi: 10.1111/josh.12049

Gao, T. (2018). Investigation of the Practices of Australian OESCs to Maintain Internet Security for Elementary and Middle School Students. Master’s thesis, Southwest University, Chongqing.

Gibb, Z. G., and Devereux, P. G. (2014). Who does that anyway? Predictors and personality correlates of cyberbullying in college. Comput. Hum. Behav. 38, 8–16. doi: 10.1016/j.chb.2014.05.009

Giménez-Gualdo, A. M., Hunter, S. C., Durkin, K., Arnaiz, P., and Maquilón, J. J. (2015). The emotional impact of cyberbullying: differences in perceptions and experiences as a function of role. Comput. Educ. 82, 228–235. doi: 10.1016/j.compedu.2014.11.013

Gini, G., and Pozzoli, T. (2009). Association between bullying and psychosomatic problems: a meta-analysis. Pediatrics 123, 1059–1065. doi: 10.1542/peds.2008-1215

Goodboy, A. K., and Martin, M. M. (2015). The personality profile of a cyberbully: examining the dark triad. Comput. Hum. Behav. 49, 1–4. doi: 10.1016/j.chb.2015.02.052

Guarini, A., Passini, S., Melotti, G., and Brighi, A. (2012). Risk and protective factors on perpetration of bullying and cyberbullying. Studia Educacyjne NR 23. Studia Edukacyjne 23, 33–55.

Hao, L. J. (2014). Theoretical analysis and ethical adjustment of college students’ online communication. Audio Vis. Educ. Res. 35, 36–41. doi: 10.13811/j.cnki.eer.2014.10.006

Hayton, A. C. (2017). Understanding Factors that Impact Cyberbullying Offending and Victimization. Ph.D. dissertation, University of New Haven, New Haven, CT.

Hee, O. C. (2014). Validity and reliability of the big five personality traits scale in Malaysia. Int. J. Innov. Appl. Stud. 5, 309–315.

Hinduja, S., and Patchin, J. W. (2008). Cyberbullying: an exploratory analysis of factors related to offending and victimization. Deviant Behav. 29, 129–156. doi: 10.1080/01639620701457816

Holland, D. N. N. (2012). The Internet Regression. Available online at: http://www.psychomedia.it/pm/telecomm/telematic/holland2.htm (accessed January 15, 2012).

Hou, M. H. Z. (2017). Relationship between Internet Addiction and Cyberbullying Among Junior High School Students. Ph.D. dissertation, Shaanxi Normal University, Xi’an.

Howard, P. J., Medina, P. L., and Howard, J. M. (1996). “The big-five locator: a quick assessment tool for consultants and trainers,” in The 1996 Annual , ed. J. W. Pfeiffer (San Diego, CA: Pfeiffer & Company), 1–1310.

Hsu, W. T., and Wang, C. H. (2010). Investigation of the Correlation between Cyberbullying and Traditional Bullying among Middle School Students. Master’s thesis, National Changhua Normal University, Changhua.

Huang, Y. Y., and Chou, C. (2010). An analysis of multiple factors of cyberbullying among junior high school students in Taiwan. Comput. Hum. Behav. 26, 1581–1590. doi: 10.1016/j.chb.2010.06.005

iiMedia Research (2018). 2018 Chinese College Students online Leisure Entertainment Behavior Monitoring Analysis Report. Available online at: https://report.iimedia.cn/repo13-0/34426.html (accessed February 1, 2021).

Ivester, M. (2011). Lol…OMG!: What Every Student Needs to Know About Online Reputation Management, Digital Citizenship and Cyberbullying. Reno, NV: Serra Knight Publishing.

Kiesler, S., Zubrow, D., Moses, A. M., and Geller, V. (1985). Affect in computer-meditated communication: an experiment in synchronous terminal-to-terminal discussion. Hum. Comput. Interact. 1, 77–104. doi: 10.1207/s15327051hci0101_3

Kowalski, R. M., Giumetti, G. W., Schroeder, A. N., and Lattanner, M. R. (2012a). Bullying in the digital age: a critical review and meta-analysis of cyberbullying research among youth. Psychol. Bull. 140, 1073–1137. doi: 10.1037/a0035618

Kowalski, R. M., Limber, S. P., and Agatston, P. W. (2012b). Cyber Bullying: Bullying in the Digital Age , 2nd Edn. Malden, MA: Wiley-Blackwell.

Li, Q. (2007). New bottle but old wine: a research of cyberbullying in schools. Comput. Hum. Behav. 23, 1777–1791. doi: 10.1016/j.chb.2005.10.005

Liang, F. (2019). How commonness and rational belief of cyber-bullying affect cyber-bullying behavior among middle school students: the mediated role of moral disengagement. Psychol. Res. 12, 278–285.

Lin, Y. (2017). Study on the Prevention of Youth Cyberbullying from the Perspective of Digital Citizenship Education. Master’s thesis, Zhejiang University of Technology, Hangzhou.

Liu, Y., and Xu, W. (2019). The Psychological factors and treatment of cyberbullying. Adv. Psychol. 9, 789–799. doi: 10.12677/AP.2019.95097

Low, S., and Espelage, D. (2013). Differentiating cyber bullying from non-physical bullying: commonalities across race, individual, and family predictors. Psychol. Violence 3, 39–52. doi: 10.1037/a0030308

Markward, M. J., Cline, S., and Markward, N. J. (2001). Group socialization, the Internet and school shootings. Int. J. Adolesc. Youth 10, 135–146. doi: 10.1080/02673843.2001.9747895

Monks, C. P., Mahdavi, J., and Rix, K. (2016). The emergence of cyberbullying in childhood: parent and teacher perspectives. Psicol. Educ. 22, 39–48. doi: 10.1016/j.pse.2016.02.002

Murwani, E. (2019). Cyberbullying behavior patterns in adolescents in Jakarta. J. Komunikasi Ikatan Sarjana Komunikasi Indonesia 4, 96–103. doi: 10.25008/jkiski.v4i2.330

Nurlita, F., Subagja, A. R. R., Munawar, H., and Mulyani, V. G. (2018). Analyzing factors affecting cyberbullying among students: its implication on social marketing. Ind. Res. Workshop Natl. Semin. 9, 805–813. doi: 10.35313/irwns.v9i0.1153

Ortega, R., Elipe, P., Mora-Merchán, J. A., Calmaestra, J., and Vega, E. (2009). The emotional impact on victims of traditional bullying and cyberbullying: a study of Spanish adolescents. Z. Psychol. 217, 197–204. doi: 10.1027/0044-3409.217.4.197

Ozden, M. S., and Icellioglu, S. (2014). The perception of cyberbullying and cybervictimization by university students in terms of their personality factors. Procedia Soc. Behav. Sci. 116, 4379–4383. doi: 10.1016/j.sbspro.2014.01.951

Park, S., Na, E. Y., and Kim, E. M. (2014). The relationship between online activities, netiquette and cyberbullying. Child. Youth Serv. Rev. 42, 74–81. doi: 10.1016/j.childyouth.2014.04.002

Pawlikowski, M., Altstötter-Gleich, C., and Brand, M. (2013). Validation and psychometric properties of a short version of Young’s Internet Addiction Test. Comput. Hum. Behav. 29, 1212–1223. doi: 10.1016/j.chb.2012.10.014

Peluchette, J. V., Karl, K., Wood, C., and Williams, J. (2015). Cyberbullying victimization: Do victims’ personality and risky social network behaviors contribute to the problem? Comput. Hum. Behav. 52, 424–435. doi: 10.1016/j.chb.2015.06.028

Peterson, J., and Densley, J. (2017). Cyber violence: What do we know and where do we go from here? Aggress. Violent Behav. 34, 193–200. doi: 10.1016/j.avb.2017.01.012

Pillay, C. L. (2012). Behavioural and Psychosocial Factors Associated with Cyberbullying. Master’s thesis, University of Zululand, Mhlathuze.

Renati, R., Berrone, C., and Zanetti, M. (2012). Morally disengaged and unempathic: Do cyberbullies fit these definitions? An exploratory study. Cyberpsychol. Behav. Soc. Netw. 15, 391–398. doi: 10.1089/cyber.2012.0046

Ribble, M. (2015). Digital Citizenship in Schools: Nine Elements all Students Should Know , 3rd Edn. Washington, DC: International Society for Technology in Education.

Rosenberg, M. (1965). Society and The Adolescent Self-Image. Princeton, NJ: Princeton University Press.

Safaria, T. (2016). Prevalence and impact of cyberbullying in a sample of indonesian junior high school students. Turk. Online J. Educ. Technol. 15, 82–90.

Schulte-Rüther, M., Markowitsch, H. J., Shah, N. J., Fink, G. R., and Piefke, M. (2008). Gender differences in brain networks supporting empathy. Neuroimage 42, 393–403. doi: 10.1016/j.neuroimage.2008.04.180

Semerci, A. (2017). Investigating the effects of personality traits on cyberbullying. Pegem Eğitim Öğretim Dergisi 7, 211–230. doi: 10.14527/pegegog.2017.008

Silva, A. D., Taveira, M. C., Marques, C., and Gouveia, V. V. (2015). Satisfaction with life scale among adolescents and young adults in portugal: extending evidence of construct validity. Soc. Indic. Res. 120, 309–318. doi: 10.1007/s11205-014-0587-9

Slonje, R., and Smith, P. K. (2008). Cyberbullying: Another main type of bullying? Scand. J. Psychol. 49, 147–154. doi: 10.1111/j.1467-9450.2007.00611.x

Smith, P. K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S., and Tippett, N. (2008). Cyberbullying: its nature and impact in secondary school pupils. J. Child Psychol. Psychiatry 49, 376–385. doi: 10.1111/j.1469-7610.2007.01846.x

Song, Y. (2015). Cyberbullying and the responsibility of schools. J. China Youth Coll. Polit. Sci. 34, 56–60. doi: 10.16034/j.cnki.10-1318/c.2015.04.011

Sourander, A., Klomek, A. B., Ikonen, M., Lindroos, J., Luntamo, T., Koskelainen, M., et al. (2010). Psychosocial risk factors associated with cyberbullying among adolescents. Arch. Gen. Psychiatry 67, 720–728. doi: 10.1001/archgenpsychiatry.2010.79

Souza, S. B., Veiga Simão, A. M., Ferreira, A. I., and Ferreira, P. C. (2018). University students’ perceptions of campus climate, cyberbullying and cultural issues: implications for theory and practice. Stud. High. Educ. 43, 2072–2087. doi: 10.1080/03075079.2017.1307818

Sticca, F., Ruggieri, S., Alsaker, F., and Perren, S. (2013). Longitudinal risk factors for cyberbullying in adolescence. J. Community Appl. Soc. Psychol. 23, 52–67. doi: 10.1002/casp.2136

Sun, S., and Deng, S. (2016). Adolescents’ cyberbullying: causes, harms, and countermeasures. Mod. Commun. 38, 144–148. doi: 10.3969/j.issn.1007-8770.2016.02.026

Topcu, C., and Erdur-Baker, O. (2010). The revised cyber bullying inventory (RCBI): validity and reliability studies. Procedia Soc. Behav. Sci. 5, 660–664. doi: 10.1016/j.sbspro.2010.07.161

Topcu, C., and Erdur-Baker, O. (2012). Affective and cognitive empathy as mediators of gender differences in cyber and traditional bullying. Sch. Psychol. Int. 33, 550–561. doi: 10.1177/0143034312446882

Vranjes, I., Baillien, E., Vandebosch, H., Erreygers, S., and De Witte, H. (2017). The dark side of working online: towards a definition and an emotion reaction model of workplace cyberbullying. Comput. Hum. Behav. 69, 324–334. doi: 10.1016/j.chb.2016.12.055

Wang, H., Zhou, X. L., Lu, C. Y., Wu, J., Deng, X. Q., Hong, L. Y., et al. (2012). Adolescent bullying involvement and psychosocial aspects of family and school life: a cross-sectional study from Guangdong Province in China. PLoS One 7:e38619. doi: 10.1371/journal.pone.0038619

Washington, E. T. (2015). An overview of cyberbullying in higher education. Adult Learn. 26, 21–27. doi: 10.1177/1045159514558412

Weaver, A. J., and Lewis, N. (2012). Mirrored morality: an exploration of moral choice in video games. Cyberpsychol. Behav. Soc. Netw. 15, 610–614. doi: 10.1089/cyber.2012.0235

Wong, D. S. W., Chan, H. C., and Cheng, C. H. K. (2014). Cyberbullying perpetration and victimization among adolescents in Hong Kong. Child. Youth Serv. Rev. 36, 133–140. doi: 10.1016/j.childyouth.2013.11.006

Yang, H., Xu, J., and Zheng, X. D. (2016). Digital citizenship in the information age. China Educ. Technol. 1, 9–16. doi: 10.3969/j.issn.1006-9860.2016.01.002

Ybarra, M. L., and Mitchell, K. J. (2004). Youth engaging in online harassment: associations with caregiver-child relationships, Internet use, and personal characteristics. J. Adolesc. 27, 319–336. doi: 10.1016/j.adolescence.2004.03.007

You, Y. (2013). Questionnaire Revision and Influencing Factors Analysis of Cyberbullying Behavior. Master’s thesis, Zhejiang Normal University, Hangzhou.

Zhang, F. F., Dong, Y., and Wang, K. (2010). Reliability and validity of the chinese version of the interpersonal reactivity Index-C. Chin. J. Clin. Psychol. 18, 155–157. doi: 10.3969/j.issn.1005-9202.2014.20.130

Zhao, J. Z., and Wang, M. H. (2019). The relationship between self-presentation in social network and life satisfaction of undergraduates: intermediacy role of the self-concept clarity. J. Baoding Univ. 32, 126–131. doi: 10.13747/j.cnki.bdxyxb.2019.05.018

Zheng, Y. X., Zhong, J. P., Huang, L. H., and Yang, H. (2020). Theoretical basis and training system of digital citizenship. China Educ. Technol. 5, 69–79. doi: 10.3969/j.issn.1006-9860.2020.05.017

Zhu, H., Shi, F. C., An, L., Yin, X. S., Fu, M. H., Wang, Y. D., et al. (2016). Analysis on prevalence of cyberbullying in college students in China. J. Jilin Univ. Med. Ed. 42, 605–611. doi: 10.13481/j.1671-587x.20160337

Keywords : cyberbullying, college student, influencing factors, digital citizenship, individual students

Citation: Zhong J, Zheng Y, Huang X, Mo D, Gong J, Li M and Huang J (2021) Study of the Influencing Factors of Cyberbullying Among Chinese College Students Incorporated With Digital Citizenship: From the Perspective of Individual Students. Front. Psychol. 12:621418. doi: 10.3389/fpsyg.2021.621418

Received: 26 October 2020; Accepted: 09 February 2021; Published: 04 March 2021.

Reviewed by:

Copyright © 2021 Zhong, Zheng, Huang, Mo, Gong, Li and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yunxiang Zheng, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 29 April 2024

Problematic social media use mediates the effect of cyberbullying victimisation on psychosomatic complaints in adolescents

• Prince Peprah 1 , 2 ,
• Michael Safo Oduro 3 ,
• Godfred Atta-Osei 4 ,
• Isaac Yeboah Addo 5 , 6 ,
• Anthony Kwame Morgan 7 &
• Razak M. Gyasi 8 , 9  

Scientific Reports volume  14 , Article number:  9773 ( 2024 ) Cite this article

Metrics details

• Public health
• Risk factors

Adolescent psychosomatic complaints remain a public health issue globally. Studies suggest that cyberbullying victimisation, particularly on social media, could heighten the risk of psychosomatic complaints. However, the mechanisms underlying the associations between cyberbullying victimisation and psychosomatic complaints remain unclear. This cross-cultural study examines the mediating effect of problematic social media use (PSMU) on the association between cyberbullying victimisation and psychosomatic complaints among adolescents in high income countries. We analysed data on adolescents aged 11–16.5 years (weighted N = 142,298) in 35 countries participating in the 2018 Health Behaviour in School-aged Children (HBSC) study. Path analysis using bootstrapping technique tested the hypothesised mediating role of PSMU. Results from the sequential binary mixed effects logit models showed that adolescents who were victims of cyberbullying were 2.39 times significantly more likely to report psychosomatic complaints than those who never experienced cyberbullying (AOR = 2.39; 95%CI = 2.29, 2.49). PSMU partially mediated the association between cyberbullying victimisation and psychosomatic complaints accounting for 12% ( \(\beta\)  = 0.01162, 95%CI = 0.0110, 0.0120) of the total effect. Additional analysis revealed a moderation effect of PSMU on the association between cyberbullying victimisation and psychosomatic complaints. Our findings suggest that while cyberbullying victimisation substantially influences psychosomatic complaints, the association is partially explained by PSMU. Policy and public health interventions for cyberbullying-related psychosomatic complaints in adolescents should target safe social media use.

Introduction

Adolescence is noted to be a critical developmental stage, with many problems, including loneliness 1 , poor friendships, an adverse class climate, school pressure 2 , suicidal ideation and attempts, and psychosomatic complaints 3 . Psychosomatic complaint is a combination of physical ailments (i.e., headaches, stomach aches, fatigue, and muscle pain) caused or exacerbated by psychological factors such as stress, irritability, anxiety, or emotional distress 4 , 5 . Psychosomatic complaints are common among adolescents, and recent estimates indicate that the global prevalence of psychosomatic complaints ranges between 10 and 50% 6 . Also, an increase in self-reported psychosomatic complaints and related mental health complaints have been reported in adolescents from high-income countries 7 , 8 . The high prevalence of psychosomatic complaints is of concern as psychosomatic complaints have severe implications for multiple detrimental health outcomes, healthcare expenditure, and quality of life of young people 9 . Thus, it is of utmost importance to identify the proximate risk factors for psychosomatic complaints among young people to aid in developing targeted interventions to reduce the incidence of psychosomatic complaints, mainly in high-income countries.

While extant research has identified risk factors for psychosomatic complaints, including malnutrition, low physical activity, and poor parental guidance 10 , 11 , 12 , one understudied but potentially important risk factor is cyberbullying victimisation. Cyberbullying victimisation is an internet-based aggressive and intentional act of continually threatening, harassing, or embarrassing individuals who cannot defend themselves using electronic contact forms such as emails, text messages, images, and videos 13 , 14 . Indeed, being typical of interpersonal interactions, cyberbullying victimisation has shown a rising trend, particularly during adolescence 15 . International literature has shown the prevalence of cyberbullying victimisation to be between 12 and 72% among young people 14 , 16 . It may be hypothesised that cyberbullying victimisation potentially increases the risk of psychosomatic complaints through factors such as problematic social media use (PSMU) 17 , 18 . However, studies are needed to identify whether and the extent to which such factors mediate the potential association of cyberbullying victimisation with psychosomatic complaints among young people.

Given this background, the present study aimed to investigate the association between cyberbullying victmisation and psychosomatic complaints in 142,298 young people aged 11–16.5 years from 35 high-income countries. A further aim was to quantify how PSMU mediates the association between cyberbullying victimisation and psychosomatic complaints.

Cyberbullying victimisation and adolescents’ psychosomatic complaints

Research has consistently shown that cyberbullying victimisation significantly impacts adolescents’ mental health 19 . For example, Kowalski and Limber 20 found that cyberbullying victimisation is associated with increased levels of depression, anxiety, and social anxiety, as well as psychosomatic complaints, such as fatigue and muscle tension. Further, studies have shown that cyberbullying victimisation and perpetration can lead to a variety of physical, social, and mental health issues, including substance abuse and suicidal thoughts and attempts 21 , 22 , 23 , 24 . Furthermore, cyberbullying victimisation is strongly associated with suicidal thoughts and attempts, regardless of demographic factors like gender or age 21 , 25 . These findings underscore the urgent need for interventions that address the mental health consequences of cyberbullying, particularly for adolescents, who are most vulnerable to its harmful effects. The findings also suggest that cyberbullying might be a potential underlying predictor of higher psychosomatic disorders among adolescents. This present study, therefore, hypothesises that H1: there is a statistically significant association between cyberbullying victimisation (X) and psychosomatic complaints (Y) (total effect).

The role of adolescents’ PSMU

Problematic Social Media Use (PSMU), a subtype of problematic internet use, refers to the uncontrolled, compulsive or excessive engagement with social media platforms such as Facebook and Twitter, characterised by addictive behaviours like mood alteration, withdrawal symptoms, and interpersonal conflicts. This pattern of social media usage can result in functional impairments and adverse outcomes 26 . Scholars and professionals have shown great concern about the length of time adolescents spend on social media. Studies have observed that (early) adolescence could be a crucial and sensitive developmental stage in which adolescent users might be unable to avoid the harmful impacts of social media use 27 . According to current research, PSMU may increase adolescents’ exposure to cyberbullying victimisation, which can have severe consequences for their mental health 28 , 29 , 30 . Similarly, an association between PSMU and physical/somatic problems, as well as somatic disorders, has been established in many studies 31 , 32 . Hanprathet et al. 33 demonstrated the negative impact of problematic Facebook use on general health, including somatic symptoms, anxiety, insomnia, depression, and social dysfunction. According to Cerutti et al. 34 , adolescents with problematic social media usage have more somatic symptoms, such as stomach pain, headaches, sore muscles, and poor energy, than their counterparts. Hence, inadequate sleep may be associated with PSMU, harming both perceived physical and mental health 35 , 36 . Again, supporting the above evidence, the relationship between PSMU, well-being, and psychological issues have been highlighted in meta-analytic research and systematic reviews 27 , 31 , 37 , 38 . Thus, this study proposes the following hypothesis: H2: there is a specific indirect effect of cyberbullying victimisation (X) on psychosomatic complaints (Y) through PSMU (M1) (indirect effect a 1 b 1 ).

Study, sample, and procedures

This study used data from the 2018 Health Behaviour in School-aged Children (HBSC) survey conducted in 35 countries and regions across Europe and Canada during the 2017–2018 academic year 39 . The HBSC research team/network is an international alliance of researchers collaborating on a cross-national survey of school students. The HBSC collects data every four years on 11-, 13- and 15- year-old adolescent boys’ and girls’ health and well-being, social environments, and health behaviours. The sampling procedure for the 2018 survey followed international guidelines 40 , 41 . A systematic sampling method was used to identify schools in each region from the complete list of both public and private schools. Participants were recruited through a cluster sampling approach, using the school class as the primary sampling unit 42 . Some countries oversampled subpopulations (e.g., by geography and ethnicity), and standardised weights were created to ensure representativeness of the population of 11, 13, and 15 years 43 . Questionnaires were translated based on a standard procedure to allow comparability between the participating countries. Our analysis used data from 35 countries and regions with complete data on cyberbullying victimisation, PSMU, and psychosomatic complaints. The study complies with ethical standards in each country and follows ethical guidelines for research and data protection from the World Health Organisation and the Organisation for Economic Co-operation and Development. Depending on the country, active or passive consent was sought from parents or legal guardians and students which was checked by teachers to participate in the study. The survey was conducted anonymously and participation in the study was voluntary for schools and students. Schools, children and adolescents could refuse to participate or withdraw their consent until the day of the survey. Moreover, all participating students were free to cease filling out the questionnaire at any moment, or to answer only selected questions. More detailed information on the methodology of the HBSC study including ethics and data protection can be found elsewhere 44 , 45 .

Outcome variable: psychosomatic complaints

Psychosomatic complaints was assessed by one collective item asking students how often they had experienced the following complaints over the past six months: headache, stomach aches, feeling low, irritability or bad mood, feeling nervous, dizziness, abdominal pain, sleep difficulty, and backache. Response options included: about every day, more than once a week, about every week, about every month, and rarely or never. This scale has sufficient test–retest reliability and validity 46 , good internal consistency (Cronbach’s a = 0.82) 47 , and has been applied in several multiple country analyses 48 , 49 . The scale is predictive of emotional problems and suicidal ideation in adolescents 50 , 51 . For our analysis, the scale was dichotomised with two or more complaints several times a week or daily coded as having psychosomatic complaints 47 , 49 .

Exposure variable: Cyberbullying victimisation

Cyberbullying victimisation is the exposure variable in this study. Thus, the exposure variable pertains to only being a victim of cyberbullying and does not include perpetration of cyberbullying. Students were first asked to read and understand a short definition of cyberbullying victimisation. They were then asked how often they were bullied over the past two months (e.g., someone sending mean instant messages, emails, or text messages about you; wall postings; creating a website making fun of you; posting unflattering or inappropriate pictures of you online without your permission or sharing them with others). Responses included: “ I have not   been  cyberbullied”, “once or twice”, “two or three times a month”, “about once a week”, and “several times a week”. These were dichotomised into “never" or “once or more". This measure of bullying victimisation has been validated across multiple cultural settings 43 , 52 , 53 , 54 .

Mediating variable

Problematic social media use (PSMU) was assessed with the Social Media Disorder Scale (Cronbach’s a = 0.89) 55 . The scale contains nine dichotomous (yes/no) items describing addiction-like symptoms, including preoccupation with social media, dissatisfaction about lack of time for social media, feeling bad when not using social media, trying but failing to spend less time using social media, neglecting other duties to use social media, frequent arguments over social media, lying to parents or friends about social media use, using social media to escape from negative feelings, and having a severe conflict with family over social media use. In this study, the endorsement of six or more items indicated PSMU as evidence suggests that a threshold of six or more is an indicative of PSMU 54 , 56 . This scale has been used across cultural contexts 43 , 52 , 54 .

Informed by previous studies 43 , 54 , 57 , the analysis controlled for theoretically relevant confounders, including sex (male/female) and age. Family affluence/socio-economic class was assessed using the Relative Family Affluence Scale, a validated six-item measure of material assets in the home, such as the number of vehicles, bedroom sharing, computer ownership, bathrooms at home, dishwashers at home, and family vacations) 56 , 58 . Finally, parental and peer support were measured using an eight item-measure 59 . Responses were recorded on a 7-point Likert scale (ranging from 0 indicating very strongly disagree to 6 indicating very strongly agree).

Statistical analysis

Region-specific descriptive statistics were calculated to describe the sample. Next, Pearson’s Chi-squared association test with Yates’ continuity correction was performed to examine plausible associations between psychosomatic complaints and other categorical study variables. Also, to account for the regional clustering or unobserved heterogeneity observed in the analytic sample, sequential mixed effect binary logit models with the inclusion of a random intercept were fitted to further examine the associations between psychosomatic complaints and cyberbullying victimisation as well as other considered covariates. Furthermore, a parallel mediator model was fitted to evaluate the specified hypothesis and understand the potential mechanism linking cyberbullying victimisation and psychosomatic complaints. More specifically, cyberbullying victimisation (X) was modelled to directly influence psychosomatic complaints (Y) and indirectly via PSMU (M). Since core variables were binary, paths could be estimated with a sequence of three logit equations: 60 , 61

where, \({i}_{1}\) , \({i}_{2}\) , and \({i}_{3}\) represent the intercept in the respective equations. The path coefficient, c, in Eq. ( 1 ) represents the total effect of predictor X on outcome Y . In Eq. ( 2 ), the path coefficient a denotes the effect of predictor X on the mediator M . Also, the c' parameter in Eq. ( 3 ) represents the direct effect of the predictor X on the response Y , adjusting for the mediator M . Lastly, the path coefficient b coefficient in Eq. ( 3 ) represents the indirect effect of the mediator M on the outcome Y , when adjusting for the predictor X . These logit models provide effect estimates on the log-odds scale, and thus can be transformed into odds ratios. Each model was adjusted for the potential confounding variables.

All statistical analyses were performed using R Software (v4.1.2; R Core Team 2021) with \(\alpha\)  =  0.05 as the significance level. More specifically, the package “mediation” in R 62 was used for the mediation analysis to estimate direct, indirect, and total effects. Inference is based on a non-parametric, 95% bias-corrected and accelerated (BCa) bootstrapped confidence interval 63 , 64 . Bootstrapping for indirect effects was set at 1000 samples, and once the 95% bootstrapped CI of the mediation effects did not include zero (0), it was deemed statistically significant. We also conducted further analysis by including an interaction between cyberbullying victimisation and PSMU to obtain insights analogous to the mediation model.

Ethics approval and consent to participate

The research was exclusively based on data sourced from the World Bank, which adheres to rigorous ethical standards in its data collection processes. Therefore, no separate ethical approval was sought or deemed necessary. Ethical approval was not required for this study since the data used for this study are secondary data. Necessary permissions and survey data were obtained from the World Bank. The World Bank data collection process upheld ethical standards and relevant guidelines in the research process including informed consent from all subjects and/or their legal guardian(s).

Preliminary analyses

The final analytic sample comprised complete information on 142,298 adolescents from 35 high-income countries (Table 1 ). The median age of the sample was 13.6 years. Most participants resided in Wales (6.26%) and the Czech Republic (6.16%). Notably, the prevalence of cyberbullying victimisation was 26.2%, and the majority (53%) were females. As observed in Table 2 , 84.6% of the participants self-reported high levels of psychosomatic complaints. Furthermore, among the participants who experienced PSMU, about 81.16% reported high levels of psychosomatic complaints. About 84.47% of the participants indicated receiving parental and peer support (see Table 2 ).

Main analyses

Results from the sequential binary mixed effects logit model are shown in Table 3 . In the first step, we included only cyberbullying victimisation in the model. We found that cyberbullying victims were 2.430 times more likely to report psychosomatic complaints than those who were not cyberbullied (OR = 2.430; 95%CI = 2.330, 2.530). The second step included sex, PSMU, parental and peer support, and family affluence as covariates. We found that cyber bullying victims were 2.390 times significantly more likely to report psychosomatic complaints than those who never experienced cyberbullying (AOR = 2.390; 95%CI = 2.29, 2.49). Additionally, the third model, which is an additional analysis involved the inclusion of an interaction between and cyberbullying victimisation and PSMU. The results showed that PSMU moderates the association between cyberbullying victimisation and psychosomatic complaints. Adolescents who were cyberbullied but did not report PSMU had reduced odds of psychosomatic complaints compared to those with PSMU (AOR = 1.220; 95%CI = 1.110–1.350). Furthermore, a caterpillar plot of empirical Bayes residuals of the models for the random intercept, region/country is obtained and shown in Fig.  1 . This represents individual effects for each country and offers additional insights into the extent of psychosomatic complaints heterogeneity across different countries. The plots visually demonstrates that regional variation for psychosomatic complaints does exist.

A caterpillar plot of empirical Bayes residuals of the models for the random intercept, region/country. This represents individual effects for each region/country. Region or country abbreviations in the figure are as follows: [AL] Albania, [AZ] Azerbaijan, [AT] Austria, [BE-VLG] Vlaamse Gewest (Belgium), [BE-WAL] Wallone, Région (Belgium), [CA] Canada, [CZ] Czech Republic, [DE] Germany, [EE] Estonia, [CA] Canada, [ES] Spain, [FR] France, [GB-ENG] England, [GB-SCT] Scotland, [GB-WLS] Wales, [GE] Georgia, [GR] Greece, [HR] Croatia, [HU] Hungary, [IE] Ireland, [IL] Israel, [IS] Iceland, [IT] Italy, [KZ] Kazakhstan, [LT] Lithuania, [LU] Luxembourg, [MD] Moldova, [MT] Malta, [NL] Netherlands, [PT] Portugal, [RO] Romania, [RS] Serbia, [RU] Russia, [SE] Sweden, [SI] Slovenia, [TR] Turkey, [LU] Luxembourg and [UA] Ukraine.

Figure  2 shows the adjusted parallel mediation results. The effect of cyberbullying victimisation on psychosomatic complaints was significantly mediated by PSMU. The paths from cyberbullying victimisation to PSMU (a: \(\beta\) =0.648, p < 0.001), PSMU to psychosomatic complaints (b: \(\beta\) =0.889, p < 0.001), and that of cyberbullying victimisation to 0.8069 (c′: \(\beta\) =0.051, p < 0.001) were also statistically significant.

A parallel mediation model of the influence of PSMU on the association between Cyberbullying Victimisation and Psychosomatic Complaints. a = path coefficient of the effect of exposure on the mediator. b = path coefficient of the effect of the mediator on the outcome. c’ = path coefficient of the direct effect of the exposure on outcome. CV, cyberbullying victimisation. PC, psychosomatic complaints.

Bootstrapping test of mediating effects

The total, direct, and indirect effects of the mediation model based on nonparametric bootstrap are presented in Table 4 . We observe that the estimated CI did not include zero (0) for any effects. This observation suggests a statistically significant indirect effect of cyberbullying victimisation on psychosomatic complaints via PSMU ( \(\beta\)  = 0.01162, 95%CI = 0.0110, 0.0120), yielding 12% of the total effect.

Key findings

This cross-cultural study examined the direct and indirect associations of cyberbullying victimisation with psychosomatic complaints via PSMU among adolescents. The results showed that cyberbullying victimisation independently influenced the experience of psychosomatic complaints. Specifically, adolescents who were victims of cyberbullying were more than two times more likely to report psychosomatic complaints. Crucially, our mediation analyses indicated that PSMU explain approximately 12% of the association between cyberbullying victimisation and psychosomatic complaints. In a further analysis, PSMU moderated the association between cyberbullying victimisation and psychosomatic complaints. This study is the first to examine the direct and indirect associations between cyberbullying victimisation and psychosomatic complaints through PSMU in adolescents across multiple high-income countries.

Interpretation of the findings

Our results confirmed the first hypothesis that there is a statistically significant direct association between cyberbullying victimisation and psychosomatic complaints. Thus, we found that cyberbullying independently directly affected the adolescents' experience of psychosomatic complaints. Previous studies have mainly focused on the direct effect of traditional face-to-face bullying on psychosomatic complaints 20 , 65 or compared the impact of traditional face-to-face bullying to cyberbullying concerning mental health 19 , 66 , 67 , 68 , 69 . A systematic review of traditional bullying and cyberbullying victimisation offers a comprehensive synthesis of the consequences of cyberbullying on adolescent health 19 . Another review suggested that cyberbullying threatened adolescents’ well-being and underscored many studies that have demonstrated effective relationships between adolescents’ involvement in cyberbullying and adverse health outcomes 70 . Other population-based cross-sectional studies have similarly shown that victims of cyberbullying experience significant psychological distress and feelings of isolation, which can further exacerbate their physical and mental health challenges 22 , 71 , 72 . The present study builds on the previously published literature by highlighting the effect of cyberbullying victimisation on adolescent psychosomatic complaints and the extent to which the association is mediated by PSMU.

Consistent with the second hypothesis, we found that PSMU mediated about 12% of the association between cyberbullying victimisation and psychosomatic complaints in this sample. While studies on the mediational role of PSMU in the relationship between cyberbullying victimisation and psychosomatic complaints are limited, evidence shows significant interplay among PSMU, cyberbullying victimisation, and psychosomatic complaints. For example, a study of over 58,000 young people in Italy found that PSMU was associated with increased levels of multiple somatic and psychological symptoms, such as anxiety and depression. 73 Another study of 1707 adolescents in Sweden found that cyberbullying victimisation was associated with increased depressive symptoms and the lowest level of subjective well-being 74 .

Other possible mediators of the cyberbullying victimisation-psychosomatic complaints association may include low self-esteem, negative body image, emotion regulation difficulties, social support, and personality traits such as neuroticism and impulsivity 20 , 67 , 72 , 75 , 76 . For example, Schneider et al. 75 have shown that emotional distress could increase psychosomatic symptoms such as headaches, stomach aches, and muscle tension. In addition, social isolation can lead to social withdrawal and a decreased sense of belonging 78 , 79 . Therefore, it is essential to explore these variables further and develop effective interventions and prevention strategies to address these interrelated factors and reduce their negative impact on adolescent health and well-being.

In a further analysis, the results show that PSMU does not only mediate but also moderate the association between cyberbullying victimisation and psychosomatic complaints among adolescents. Specifically, cyberbullied adolescents with no report of PSMU had reduced likelihoods of experiencing psychosomatic complaints compared to those with PSMU. This result is interesting and could be due to several factors. First, individuals with PSMU may already be experiencing heightened levels of psychological distress due to their excessive social media use, making them more vulnerable to the negative effects of cyberbullying 80 , 81 , 82 . For instance, excessive time spent on social media, particularly in activities such as comparing oneself to others or seeking validation through likes and comments, has been linked to increased psychological distress 83 , 84 . Conversely, the finding that cyberbullied adolescents without PSMU had reduced likelihoods of experiencing psychosomatic complaints compared to those with PSMU suggests a protective effect of lower social media use. Adolescents who are not excessively engaged with social media may have fewer opportunities for exposure to cyberbullying and may also have healthier coping strategies in place to deal with any instances of online victimisation 43 , 85 , 86 .

The results suggest that professionals in the fields of education, counselling, and healthcare should prioritise addressing the issue of cyberbullying victimisation when assessing the physical and psychological health of adolescents. Evidently, adolescents who experience cyberbullying require support. Thus, proactive measures are essential, and support could be provided by multiple professional communities that serve adolescents and young people in society, such as educational, behavioural health, and medical professionals. Sensitive inquiry regarding cyberbullying experiences is necessary when addressing adolescent health issues such as depression, substance use, suicidal ideation, and somatic concerns 19 . Our findings underscore the need for comprehensive, school-based programs focused on cyberbullying victimisation prevention and intervention.

Strengths and limitations

The study's main strength lies in the use of a large sample size representing multiple countries in high income countries. This large sample size improved the representativeness and veracity of our findings. The complex research approach helps advance our understanding of the interrelationships between cyberbullying victimisation, PSMU, and psychosomatic complaints among adolescents. However, the study has its limitations. First, the cross-sectional design does not allow directionality and causal inferences. Second, retrospective self-reporting for the critical study variables could lead to recall and social desirability biases. Third, the presence of residual and unobserved confounders, despite adjusting for some covariates, can be considered a limitation of this study. Further research is needed to confirm these findings and better understand how PSMU mediates the relationship between cyberbullying victimisation and psychosomatic complaints.

Conclusions

This study has provided essential insights into the interrelationships between cyberbullying victimisation, PSMU, and psychosomatic complaints among adolescents in high income countries. The findings suggest that cyberbullying is directly associated with psychosomatic complaints and that PSMU significantly and partially mediates this association. This study also highlights the importance of addressing cyberbullying victimisation and its negative impact on adolescent health and emphasises the need to address PSMU. Overall, the study underscores the importance of promoting healthy online behaviour and providing appropriate support for adolescents who experience cyberbullying victimisation. Further studies will benefit from longitudinal data to confirm our findings.

Data availability

The data that support the findings of this study are available from the World Bank, but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. Data are, however, available from the corresponding author ([email protected]) upon reasonable request and with permission of the World Bank.

Lyyra, N., Välimaa, R. & Tynjälä, J. Loneliness and subjective health complaints among school-aged children. Scand. J. Public Health 46 (20), 87–93. https://doi.org/10.1177/1403494817743901 (2018).

Article   PubMed   Google Scholar  

Ottova, V. et al. The role of individual-and macro-level social determinants on young adolescents’ psychosomatic complaints. J. Early. 32 (1), 126–158. https://doi.org/10.1177/0272431611419510 (2012).

Article   Google Scholar  

Heinz, A., Catunda, C., van Duin, C. & Willems, H. Suicide prevention: Using the number of health complaints as an indirect alternative for screening suicidal adolescents. J. Affect. Disord. 260 , 61–66. https://doi.org/10.1016/j.jad.2019.08.025 (2020).

Högberg, B., Strandh, M. & Hagquist, C. Gender and secular trends in adolescent mental health over 24 years–the role of school-related stress. Soc. Cci Med. 250 , 112890. https://doi.org/10.1016/j.socscimed.2020.112890 (2020).

Hagquist, C., Due, P., Torsheim, T. & Välimaa, R. Cross-country comparisons of trends in adolescent psychosomatic symptoms—a Rasch analysis of HBSC data from four Nordic countries. Health Qual. Life Outcomes 17 (1), 1–13. https://doi.org/10.1186/s12955-019-1097-x (2019).

Shorey, S., Ng, E. D. & Wong, C. H. Global prevalence of depression and elevated depressive symptoms among adolescents: A systematic review and meta-analysis. Br. J. Clin. Psychol. 61 (2), 287–305. https://doi.org/10.1111/bjc.12333 (2022).

Potrebny, T. et al. Health complaints among adolescents in Norway: A twenty-year perspective on trends. PloS one 14 (1), e0210509. https://doi.org/10.1371/journal.pone.0210509 (2019).

Article   CAS   PubMed   PubMed Central   Google Scholar  

van Geelen, S. M. & Hagquist, C. Are the time trends in adolescent psychosomatic problems related to functional impairment in daily life? A 23-year study among 20,000 15–16 year olds in Sweden. J. Psychol. Res. 87 , 50–56. https://doi.org/10.1016/j.jpsychores.2016.06.003 (2016).

Swedish Association of Local Authorities and Regions and Ministry of Health and Social Affairs. Insatser inom området psykisk hälsa och suicidprevention. Överenskommelse mellan staten och Sveriges Kommuner och Regioner (SKR). Swedish Association of Local Authorities and Regions and Ministry of Health and Social Affairs. Stokholm, Sweden: 2021–2022. https://skr.se/skr/halsasjukvard/utvecklingavverksamhet/psykiskhalsa/overenskommelsepsykiskhalsa.234.html (2022).

Brooks, S. J., Feldman, I., Schiöth, H. B. & Titova, O. E. Important gender differences in psychosomatic and school-related complaints in relation to adolescent weight status. Sci. Rep. 11 (1), 14147. https://doi.org/10.1038/s41598-021-93761-0 (2021).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Whitehead, R. et al. Trends in adolescent overweight perception and its association with psychosomatic health 2002–2014: Evidence from 33 countries. J. Adol. Health 60 (2), 204–211. https://doi.org/10.1016/j.jadohealth.2016.09.029 (2017).

Nilsen, W., Karevold, E., Røysamb, E., Gustavson, K. & Mathiesen, K. S. Social skills and depressive symptoms across adolescence: Social support as a mediator in girls versus boys. J. Adol. 36 (1), 11–20. https://doi.org/10.1016/j.adolescence.2012.08.005 (2013).

Englander, E., Donnerstein, E., Kowalski, R., Lin, C. A. & Parti, K. Defining cyberbullying. Pediatric 140 (S2), 148–151. https://doi.org/10.1542/peds.2016-1758U (2017).

Chan, H. C. O. & Wong, D. S. Traditional school bullying and cyberbullying in Chinese societies: Prevalence and a review of the whole-school intervention approach. Aggress. Viol. Behav. 23 , 98–108. https://doi.org/10.1016/j.avb.2015.05.010 (2015).

Griffiths, M. D., Kuss, D. J. & Demetrovics, Z. Social networking addiction: An overview of preliminary findings. Behav Addict. 2014 , 119–141. https://doi.org/10.1016/B978-0-12-407724-9.00006-9 (2014).

Athanasiou, K. et al. Cross-national aspects of cyberbullying victimization among 14–17-year-old adolescents across seven European countries. BMC Public Health 18 , 1–15. https://doi.org/10.1186/s12889-018-5682-4 (2018).

Nagata, J. M. et al. Cyberbullying and Sleep Disturbance among Early Adolescents in the US. Acad. Pediatr. 23 (6), 1220–1225. https://doi.org/10.1016/j.acap.2022.12.007 (2022).

Fahy, A. E. et al. Longitudinal associations between cyberbullying involvement and adolescent mental health. J. Ado.l Health 59 (5), 502–509. https://doi.org/10.1016/j.jadohealth.2016.06.006 (2016).

Zych, I., Ortega-Ruiz, R. & Del Rey, R. Systematic review of theoretical studies on bullying and cyberbullying: Facts, knowledge, prevention, and intervention. Aggress. Viol. Behav. 23 , 1–21. https://doi.org/10.1016/j.avb.2015.10.001 (2015).

Kowalski, R. M. & Limber, S. P. Psychological, physical, and academic correlates of cyberbullying and traditional bullying. J. Adol. Health 53 (1), S13–S20. https://doi.org/10.1016/j.jadohealth.2012.09.018 (2013).

Van Geel, M., Vedder, P. & Tanilon, J. Relationship between peer victimization, cyberbullying, and suicide in children and adolescents: A meta-analysis. JAMA Pediatr. 168 (5), 435–442. https://doi.org/10.1001/jamapediatrics.2013.4143 (2014).

Article   CAS   PubMed   Google Scholar  

Albdour, M., Hong, J. S., Lewin, L. & Yarandi, H. The impact of cyberbullying on physical and psychological health of Arab American adolescents. J. Immig. Minor. Health 21 , 706–715. https://doi.org/10.1007/s10903-018-00850-w (2019).

Yoon, Y. et al. Association of cyberbullying involvement with subsequent substance use among adolescents. J. Adol. Health 65 (5), 613–620. https://doi.org/10.1016/j.jadohealth.2019.05.006 (2019).

Yuchang, J., Junyi, L., Junxiu, A., Jing, W. & Mingcheng, H. The differential victimization associated with depression and anxiety in cross-cultural perspective: A meta-analysis. Trauma Viol. Abuse 20 (4), 560–573. https://doi.org/10.1177/1524838017726426 (2019).

Gini, G. & Espelage, D. L. Peer victimization, cyberbullying, and suicide risk in children and adolescents. Jama 312 (5), 545–546. https://doi.org/10.1001/jama.2014.3212 (2014).

Tullett-Prado, D., Doley, J. R., Zarate, D., Gomez, R. & Stavropoulos, V. Conceptualising social media addiction: A longitudinal network analysis of social media addiction symptoms and their relationships with psychological distress in a community sample of adults. BMC Psychol. 23 (1), 1–27. https://doi.org/10.1186/s12888-023-04985-5 (2023).

Keles, B., McCrae, N. & Grealish, A. A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents. Int. J. Adol Youth 25 (1), 79–93. https://doi.org/10.1080/02673843.2019.1590851 (2020).

O’reilly, M. et al. Is social media bad for mental health and wellbeing? Exploring the perspectives of adolescents. Clin. Child Psychol. Psych. 23 (4), 601–613. https://doi.org/10.1177/1359104518775154 (2018).

Marino, C., Gini, G., Angelini, F., Vieno, A. & Spada, M. M. Social norms and e-motions in problematic social media use among adolescents. Addict. Behav. Rep. 11 , 100250. https://doi.org/10.1016/j.abrep.2020.100250 (2020).

Article   PubMed   PubMed Central   Google Scholar  

Sedgwick, R., Epstein, S., Dutta, R. & Ougrin, D. Social media, internet use and suicide attempts in adolescents. Curr. Opin. Psychol. 32 (6), 534. https://doi.org/10.1097/YCO.0000000000000547 (2019).

Marino, C., Hirst, C. M., Murray, C., Vieno, A. & Spada, M. M. Positive mental health as a predictor of problematic internet and Facebook use in adolescents and young adults. J. Happ. Stud. 19 , 2009–2022. https://doi.org/10.1007/s10902-017-9908-4 (2018).

Sarmiento, I. G. et al. How does social media use relate to adolescents’ internalizing symptoms? Conclusions from a systematic narrative review. Adol. Res. Rev. 5 , 381–404. https://doi.org/10.1007/s40894-018-0095-2 (2020).

Hanprathet, N., Manwong, M., Khumsri, J. M. S., Yingyeun, R. & Phanasathit, M. Facebook addiction and its relationship with mental health among Thai high school students. J. Med. Assoc. Thailand 98 , 81–90 (2015).

Google Scholar  

Cerutti, R. et al. Sleep disturbances partially mediate the association between problematic internet use and somatic symptomatology in adolescence. Curr. Psychol. 40 , 4581–4589. https://doi.org/10.1007/s12144-019-00414-7 (2021).

Van Den Eijnden, R., Koning, I., Doornwaard, S., Van Gurp, F. & Ter Bogt, T. The impact of heavy and disordered use of games and social media on adolescents’ psychological, social, and school functioning. J. Behav. Addit. 7 (3), 697–706. https://doi.org/10.1556/2006.7.2018.65 (2018).

Andreassen, C. S. & Pallesen, S. Social network site addiction-an overview. Curr. Pharma Des. 20 (25), 4053–4061. https://doi.org/10.2174/13816128113199990616 (2014).

Article   CAS   Google Scholar  

Andreassen, C. S. Online social network site addiction: A comprehensive review. Curr. Addit Rep. 2 (2), 175–184. https://doi.org/10.1007/s40429-015-0056-9 (2015).

Best, P., Manktelow, R. & Taylor, B. Online communication, social media and adolescent wellbeing: A systematic narrative review. Child. Youth Serv. Rev. 41 , 27–36. https://doi.org/10.1016/j.childyouth.2014.03.001 (2014).

Boer, M. et al. Adolescents’ intense and problematic social media use and their well-being in 29 countries. J. Adol. Health 66 (6), S89–S99. https://doi.org/10.1016/j.jadohealth.2020.02.014 (2020).

Inchley, J. et al . Adolescent alcohol-related behaviours: Trends and inequalities in the WHO European Region, 2002–2014: Observations from the Health Behaviour in School-aged Children (HBSC) WHO collaborative cross-national study. World Health Organization. Regional Office for Europe (2018). https://apps.who.int/iris/handle/10665/342239 .

Moor, I. et al. The 2017/18 Health Behaviour in School-aged Children (HBSC) study–mthodology of the World Health Organization’s child and adolescent health study. J. Health Monitor. 5 (3), 88. https://doi.org/10.25646/6904 (2020).

Nardone, P. et al. Dietary habits among Italian adolescents and their relation to socio-demographic characteristics. Ann. Istit. Super. Sanita 56 (4), 504–513. https://doi.org/10.4415/ANN_20_04_15 (2020).

Craig, W. et al. Social media use and cyber-bullying: A cross-national analysis of young people in 42 countries. J. Adol. Health 66 (6), S100–S108. https://doi.org/10.1016/j.jadohealth.2020.03.006 (2020).

Moor, I. et al. The 2017/18 Health Behaviour in School-aged Children (HBSC) study–methodology of the World Health Organization’s child and adolescent health study. J. Health Monitor. 5 (3), 88 (2020).

Inchley, J., Currie, D., Cosma, A. & Samdal, O. Health Behaviour in School-Aged Children (HBSC) Study Protocol: Background, Methodology and Mandatory Items for the 2017/18 Survey ; CAHRU: St Andrews, UK (2018).

Haugland, S. & Wold, B. Subjective health complaints in adolescence—reliability and validity of survey methods. J. Adol. 24 (5), 611–624. https://doi.org/10.1006/jado.2000.0393 (2001).

Khan, A., Khan, S. R. & Lee, E. Y. Association between lifestyle behaviours and mental health of adolescents: Evidence from the Canadian HBSC Surveys, 2002–2014. Int. J. Environ. Res. Public Health 19 (11), 6899. https://doi.org/10.3390/ijerph19116899 (2022).

Högberg, B., Strandh, M., Johansson, K. & Petersen, S. Trends in adolescent psychosomatic complaints: A quantile regression analysis of Swedish HBSC data 1985–2017. Scand. J. Public Health 2022 , 21094497. https://doi.org/10.1177/14034948221094497 (2022).

Bjereld, Y., Augustine, L., Turner, R., Löfstedt, P. & Ng, K. The association between self-reported psychosomatic complaints and bullying victimisation and disability among adolescents in Finland and Sweden. Scand. J. Public Health 2022 , 1089769. https://doi.org/10.1177/14034948221089769 (2022).

Heinz, A., van Duin, C., Kern, M. R., Catunda, C. & Willems, H. Trends from 2006–2018 in Health, Health Behaviour, Health Outcomes and Social Context of Adolescents in Luxembourg . University of Luxembourg (2020).  http://hdl.habndle.net/10993/42571 .

Gariepy, G., McKinnon, B., Sentenac, M. & Elgar, F. J. Validity and reliability of a brief symptom checklist to measure psychological health in school-aged children. Child Indic. Res. 9 , 471–484. https://doi.org/10.1007/s12187-015-9326-2 (2016).

Biswas, T. et al. Variation in the prevalence of different forms of bullying victimisation among adolescents and their associations with family, peer and school connectedness: A population-based study in 40 lower and middle income to high-income countries (LMIC-HICs). J. Child. Adol. Trauma 2022 , 1–11. https://doi.org/10.1007/s40653-022-00451-8 (2022).

Sasson, H., Tur-Sinai, A., Dvir, K. & Harel-Fisch, Y. The role of parents and peers in cyberbullying perpetration: Comparison among Arab and Jewish and youth in Israel. Child Indic. Res. 2022 , 1–21. https://doi.org/10.1007/s12187-022-09986-6 (2022).

Marengo, N. et al. Cyberbullying and problematic social media use: An insight into the positive role of social support in adolescents—data from the Health Behaviour in School-aged Children study in Italy. Public Health 199 , 46–50. https://doi.org/10.1016/j.puhe.2021.08.010 (2021).

Van den Eijnden, R. J. J. M., Lemmens, J. & Valkenburg, P. The social media disorder scale: Validity and psychometric properties. Comp. Hum. Behav. 61 (August), 478487. https://doi.org/10.1016/j.chb.2016.03.038 (2016).

Borraccino, A. et al. Problematic social media use and cyber aggression in Italian adolescents: The remarkable role of social support. Int. J. Environ. Res. Public Health 19 (15), 9763. https://doi.org/10.3390/ijerph19159763 (2022).

Hamre, R., Smith, O. R. F., Samdal, O. & Haug, E. Gaming behaviors and the association with sleep duration, social jetlag, and difficulties falling asleep among Norwegian adolescents. Int. J. Environ. Res. Public Health 19 (3), 1765. https://doi.org/10.3390/ijerph19031765 (2022).

Currie, C. et al. Researching health inequalities in adolescents: The development of the Health Behaviour in School-Aged Children (HBSC) family affluence scale. Soc. Sci Med. 66 (6), 1429–1436. https://doi.org/10.1016/j.socscimed.2007.11.024 (2008).

Zimet, G. D., Powell, S. S., Farley, G. K., Werkman, S. & Berkoff, K. A. Psychometric characteristics of the multidimensional scale of perceived social support. J. Person. Assess. 55 (3–4), 610–617. https://doi.org/10.1080/00223891.1990.9674095 (1990).

MacKinnon, D. P., Lockwood, C. M., Brown, C. H., Wang, W. & Hoffman, J. M. The intermediate endpoint effect in logistic and probit regression. Clin. Trial 4 (5), 499–513. https://doi.org/10.1177/1740774507083434 (2007).

Rijnhart, J. J., Valente, M. J., Smyth, H. L. & MacKinnon, D. P. Statistical mediation analysis for models with a binary mediator and a binary outcome: The differences between causal and traditional mediation analysis. Prevent. Sci. 2021 , 1–11. https://doi.org/10.1007/s11121-021-01308-6 (2021).

Tingley D, Yamamoto T, Hirose K, Keele L, Imai K, Yamamoto MT. Package ‘mediation’. Computer software manual. 2019 Sep 13:175-84.

DiCiccio, T. J. & Efron, B. Bootstrap confidence intervals. Stat. Sci. 11 (3), 189–228. https://doi.org/10.1214/ss/1032280214 (1996).

Article   MathSciNet   Google Scholar  

Preacher, K. J. & Hayes, A. F. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav. Res. Method 40 (3), 879–891. https://doi.org/10.3758/BRM.40.3.879 (2008).

Tomşa, R., Jenaro, C., Campbell, M. & Neacşu, D. Student’s experiences with traditional bullying and cyberbullying: Findings from a Romanian sample. Procedia-Soc. Behav. Sci. 78 , 586–590. https://doi.org/10.1016/j.sbspro.2013.04.356 (2013).

Baier, D., Hong, J. S., Kliem, S. & Bergmann, M. C. Consequences of bullying on adolescents’ mental health in Germany: Comparing face-to-face bullying and cyberbullying. J. Child Fam. Stud. 28 , 2347–2357. https://doi.org/10.1007/s10826-018-1181-6 (2019).

Beckman, L., Hagquist, C. & Hellström, L. Does the association with psychosomatic health problems differ between cyberbullying and traditional bullying?. Emot. Behav. Differ. 17 (3–4), 421–434. https://doi.org/10.1080/13632752.2012.704228 (2012).

Lazuras, L., Barkoukis, V. & Tsorbatzoudis, H. Face-to-face bullying and cyberbullying in adolescents: Trans-contextual effects and role overlap. Tech. Soc. 48 , 97–101. https://doi.org/10.1016/j.techsoc.2016.12.001 (2017).

Li, J., Sidibe, A. M., Shen, X. & Hesketh, T. Incidence, risk factors and psychosomatic symptoms for traditional bullying and cyberbullying in Chinese adolescents. Child. Youth Serv. Rev. 107 , 104511. https://doi.org/10.1016/j.childyouth.2019.104511 (2019).

Nixon, C. L. Current perspectives: The impact of cyberbullying on adolescent health. Adol. Health Med. Therapy 2014 , 143–158. https://doi.org/10.2147/AHMT.S36456 (2014).

Olenik-Shemesh, D., Heiman, T. & Eden, S. Cyberbullying victimisation in adolescence: Relationships with loneliness and depressive mood. Emot. Behav. Differ. 17 (3–4), 361–374. https://doi.org/10.1080/13632752.2012.704227 (2012).

Sourander, A. et al. Psychosocial risk factors associated with cyberbullying among adolescents: A population-based study. Arch. Gener. Psychiatry 67 (7), 720–728. https://doi.org/10.1001/archgenpsychiatry.2010.79 (2010).

Claudia, M. et al. Problematic social media use: Associations with health complaints among adolescents. Ann. Istit. Super. Sanità 56 (4), 514–521. https://doi.org/10.4415/ANN_20_04_16 (2020).

Hellfeldt, K., López-Romero, L. & Andershed, H. Cyberbullying and psychological well-being in young adolescence: The potential protective mediation effects of social support from family, friends, and teachers. Int.. J. Environ. Res. Public Health 17 (1), 45. https://doi.org/10.3390/ijerph17010045 (2020).

Gini, G. & Pozzoli, T. Bullied children and psychosomatic problems: A meta-analysis. Pediatrics 132 (4), 720–729. https://doi.org/10.1542/peds.2013-0614 (2013).

Landstedt, E. & Persson, S. Bullying, cyberbullying, and mental health in young people. Scand. J. Public Health 42 (4), 393–399. https://doi.org/10.1177/1403494814525 (2014).

Schneider, S. K., Odonnell, L., Stueve, A. & Coulter, R. W. Cyberbullying, school bullying, and psychological distress: A regional census of high school students. Am. J. Public Health 102 (1), 171–177. https://doi.org/10.2105/AJPH.2011.300308 (2012).

Brighi, A., Guarini, A., Melotti, G., Galli, S. & Genta, M. L. Predictors of victimisation across direct bullying, indirect bullying and cyberbullying. Emot. Behav. Differ. 17 (3–4), 375–388. https://doi.org/10.1080/13632752.2012.704684 (2012).

Cowie, H. Cyberbullying and its impact on young people’s emotional health and well-being. The Psychia 37 (5), 167–170. https://doi.org/10.1192/pb.bp.112.040840 (2013).

Berryman, C., Ferguson, C. J. & Negy, C. Social media use and mental health among young adults. Psych. Q. 89 , 307–314. https://doi.org/10.1007/s11126-017-9535-6 (2018).

Verduyn, P., Ybarra, O., Résibois, M., Jonides, J. & Kross, E. Do social network sites enhance or undermine subjective well-being? A critical review. Soc. Issue Policy Rev. 11 (1), 274–302. https://doi.org/10.1542/peds.2007-0693 (2017).

Vogel, E. A., Rose, J. P., Okdie, B. M., Eckles, K. & Franz, B. Who compares and despairs? The effect of social comparison orientation on social media use and its outcomes. Person. Individ. Differ. 86 , 249–256. https://doi.org/10.1016/j.paid.2015.06.026 (2015).

Keles, B., McCrae, N. & Grealish, A. A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents. Int. J. Adol. Youth 25 (1), 79–93. https://doi.org/10.1080/02673843.2019.1590851 (2020).

Boer, M. et al. Adolescents’ intense and problematic social media use and their well-being in 29 countries. J. Adol. Health 66 (6), 89–99. https://doi.org/10.1016/j.jadohealth.2020.02.014 (2020).

McHugh, B. C., Wisniewski, P., Rosson, M. B. & Carroll, J. M. When social media traumatizes teens: The roles of online risk exposure, coping, and post-traumatic stress. Int. Res. 28 (5), 1169–1188. https://doi.org/10.1108/IntR-02-2017-0077 (2018).

Trnka, R., Martínková, Z. & Tavel, P. An integrative review of coping related to problematic computer use in adolescence. Int. J. Public Health 61 , 317–327. https://doi.org/10.1007/s00038-015-0693-8 (2016).

Chen, L., Ho, S. S. & Lwin, M. O. A meta-analysis of factors predicting cyberbullying perpetration and victimization: From the social cognitive and media effects approach. New Media Soc. 19 (8), 1194–1213. https://doi.org/10.1177/1461444816634037 (2017).

Download references

Acknowledgements

We thank the 2017/2018 HBSC survey team/network, the coordinator and the Data Bank Manager for granting us access to the datasets. We duly acknowledge all school children who participated in the surveys.

Author information

Authors and affiliations.

Social Policy Research Centre, University of New South Wales, Sydney, Australia

Prince Peprah

Centre for Primary Health Care and Equity, University of New South Wales, Sydney, Australia

Pfizer Research and Development, PSSM Data Sciences, Pfizer, Inc., Connecticut, USA

Michael Safo Oduro

Centre for Disability and Rehabilitation Studies, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

Godfred Atta-Osei

Centre for Social Research in Health, University of New South Wales, Sydney, Australia

Isaac Yeboah Addo

Concord Clinical School, University of Sydney, Sydney, Australia

Department of Planning, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

Anthony Kwame Morgan

African Population and Health Research Center, Nairobi, Kenya

Razak M. Gyasi

National Centre for Naturopathic Medicine, Faculty of Health, Southern Cross University, Lismore, NSW, Australia

You can also search for this author in PubMed   Google Scholar

Contributions

PP: conceptualization, methodology and writing; MOS: conceptualization, statistical methodology and analysis, and writing; GA-O: conceptualization and writing; IA: methodology and writing; RMG: conceptualization and writing. AKM: writing. All the authors reviewed and to the publication of this paper.

Corresponding author

Correspondence to Anthony Kwame Morgan .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Peprah, P., Oduro, M.S., Atta-Osei, G. et al. Problematic social media use mediates the effect of cyberbullying victimisation on psychosomatic complaints in adolescents. Sci Rep 14 , 9773 (2024). https://doi.org/10.1038/s41598-024-59509-2

Download citation

Received : 27 November 2023

Accepted : 11 April 2024

Published : 29 April 2024

DOI : https://doi.org/10.1038/s41598-024-59509-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Adolescent well-being
• Psychosomatic complaints
• Cyberbullying victimisation
• Sleep quality
• Social media use

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Cyberbullying Research in South Africa

Here is the research we’ve found on cyberbullying in South Africa, with the most recent first. Please email us if you have any articles to add with the details ordered in the same format as the others.

Authors: Fereshteh Naseri, Davoud Taghvaei, Bahram Saleh Sedghpour, Gholam Ali Ahmadi

Title: A Comparative Study on the Opportunities and Threats of the Internet and Considering the Rights of Kids Online in Australia, Brazil, Iran, and South Africa

Journal: Iranian Journal of Comparative Education

URL: https://journal.cesir.ir/article_140973.html?lang=en

Abstract: The present study aims to compare the opportunities and threats of the Internet and considering the rights of kids online in Australia, Brazil, Iran, and South Africa. The research method was qualitative-comparative using Bereday’s approach. The strategy for selection of countries was “different systems, different outputs”. The population included 210 studies from which 45 samples related to research objectives were selected. Primary documents and self-assessment method were used for increasing the validity and reliability of references, respectively. John Stuart Mill’s agreement and difference method was used for data analysis and George Bereday’s method was used for presenting the results. The findings indicated that most similarities are in Internet threats and most differences are in the opportunities created for kids online and considering their rights in these countries. Cyber-bullying and Internet addiction threaten all kids online in such countries. In terms of considering the rights of kids online, Australia is at the top of the list, followed by Brazil, South Africa, and Iran. No serious measure has been taken in Iran to ensure the rights of kids online due to weak infrastructure, low internet speed, and legal gap. Based on the findings, cyberspace authorities and planners in Iran are suggested to take more legal, executive, and educational measures in the framework of international cooperation to achieve the rights and welfare of kids online.

Authors: Radebe, F. and Kyobe, M.

Title: The Response of Social Crime Prevention Police to Cyberbullying Perpetrated by Youth in Rural Areas of South Africa

Journal: Int. J. Environ. Res. Public Health

URL: https://www.mdpi.com/1660-4601/18/24/13421

Abstract: Recently, South Africa has seen a surge in violence, cyberbullying by learners against peers, and online malicious acts against teachers. In response, the South African Department of Basic Education invited the social crime prevention police to intervene. This study reports on the developmental issues contributing to cyberbullying and the police response to this violence in rural schools. An extensive literature review was conducted, and a conceptual framework was developed to guide the study and development of a mobile application. This framework was tested using data collected from focus groups, 8 police officers, 9 teachers, 52 grade-10 learners, and 27 grade-12 learners. The data were analyzed using thematic and quantitative techniques. The findings reveal some developmental issues. For instance, teachers are often targeted by learners online because they fail to take prompt action when learners report cyberbullying incidents. This finding is consistent with the developmental theory which predicts that lack of support would create a permissive context for cyberbullying. In addition, the popularity of cyberbullying has a stronger influence on older, rather than younger, adolescents. Older adolescents are more concerned about gaining popularity than being socially accepted. Recommendations are made which can be useful to schools, learners, and the police force in their fight against cyberbullying.

Author: Mong, E.

Title: Cyberbullying and its effects on the mental well-being of adolescents

Journal: Cannot find

URL: http://repository.nwu.ac.za/handle/10394/35562

Abstract: Studies investigating the effects of cyberbullying on the mental well-being of adolescents are needed to guide the development of preventive and protective measures for cyberbullying. Although a substantial number of studies have been undertaken on the prevalence of cyberbullying, research describing the effect of cyberbullying on the mental well-being and level of major depression among adolescents (for both the victim and the bully) are inconclusive for the South African context. This study was subsequently conceptualised based on a bio-ecological perspective that focuses on the hypothetical interrelationship between cyberbullying, adolescence, mental well-being and major depressive disorder. The main objective of this study was to determine the prevalence and nature of cyberbullying and its effect on and relationship to mental well-being among adolescents in the Matlosana municipal area (Dr Kenneth Kaunda district, North West province, South Africa). This quantitative research study was situated in a post-positivistic research paradigm. A survey design (which included the adapted Daphne Cyberbullying Questionnaire, the Mental Health Continuum Short Form and the Patient Health Questionnaire-9) was used to reach the aims of this study. A stratified random sampling procedure was initially used to identify participating schools, where after an availability sample was used. The sample group consisted of 187 (n) Grade 8 to 11 learners in the Matlosana municipal district in the North West province. The resulting data were analysed using descriptive statistics. Since the sample was an availability sample and not representative of the Matlosana district, generalisations to the rest of South Africa could not be made. The data analysis and interpretation included statistics pertaining to findings on adolescents’ experience of the school environment, the nature of electronics use among adolescents, the prevalence of cyberbullying and traditional bullying and the relationship between cyberbullying and traditional bullying; findings related to demographic differences with regard to cyberbullying and the nature of cyberbullying among adolescents, and lastly, findings on the effect of cyberbullying on the level of major depression and the mental well-being of the group involved in cyberbullying (both victims and bullies). The most prominent conclusions were that cyberbullying was definitely prevalent among this sample group and among South African adolescents, but cyberbullying is not a loose standing problem as it seems to be tied with traditional bullying. Yet, the anonymity and unbounded audience factors that make cyberbullying unique, contribute to the problem. Both cyberbullies and -victims in this sample suffered from major depressive disorder and they did not experience optimal mental well-being. Major risk factors of cyberbullying involvement included extensive, unrestricted and unsupervised use of electronics. It seems that adolescents need help with socialisation and relationship forming, as well as with developing useful protective strategies when they do come across cyberbullying. The study contributed to the body of scholarship on the prevalence and nature of cyberbullying and its effects on the mental well-being of adolescents (victims and bullies). The research extends the knowledge about the relationships between cyberbullying and mental well-being and cyberbullying and major depressive disorder. Various role players, such as adolescents (victims and bullies), schools, teachers, the Department of Education and parents will benefit from this study since a health promoting school approach towards online protection is recommended.

Authors: Pillay, R. and Sacks, G.

Title: Cyberbullying—A Shrouded Crime: Experiences of South African Undergraduate Students

Journal: The Oriental Anthropologist: A Bi-annual International Journal of the Science of Man

URL: https://journals.sagepub.com/doi/abs/10.1177/0972558X20952986

Abstract: Crimes in the 21st century using technology as a medium are complex and evolving rapidly. One such crime that is difficult to define is cyberbullying, which extorts an emotional impact on the victim. This qualitative, descriptive case study considers the experiences of 10 undergraduate students regarding what they self-disclosed as cyberbullying. Snowball sampling was used, and the data collected using face-to-face interviews were analyzed using content analysis. The research instrument used was a semi-structured interview schedule. Findings revealed that nine of the participants knew the identity of the bully. Some of the social media platforms used for the cyberbullying included Facebook, Mxit, and WhatsApp, whereby the types of bullying included harassment, flaming, and denigration. Some gender differences were evident in the verbalized emotions of the sample and the support systems the female participants used. This study can serve as a catalyst for further research and interventions for the development of strategies and educational programs to manage this type of bullying.

Authors: Cilliers, L. and Chinyamurindi, W.

Title: Perceptions of cyber bullying in primary and secondary schools among student teachers in the Eastern Cape Province of South Africa

Journal: THE ELECTRONIC JOURNAL OF INFORMATION SYSTEMS IN DEVELOPING COUNTRIES

URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/isd2.12131

Abstract: Cyber bullying has become a topical issue among school learners in South Africa. However, there is very little guidance for schools on how to deal with cyber bullying from the South African Department of Basic Education. This study investigated the perceptions of cyber bullying in primary and secondary schools among student teachers in the Eastern Cape. The study made use of a quantitative survey approach to collect data from 150 student teachers at a university in the Eastern Cape. The student teachers were representative of all four of the school phases. The results indicated that cyber bullying is a serious issue at the schools but that the topic has not been incorporated into policy or the school curriculum yet. The recommendation of the study is that the South African Department of Basic Education must provide a standardized policy that schools can use to implement and enforce cyber safety behavior in the schools.

Author(s): Smit, D. M.

Title: Cyberbullying in South African and American schools: A legal comparative study

Journal: South African Journal of Education

URL: http://www.scielo.org.za/pdf/saje/v35n2/01.pdf

Abstract: Bullying conjures up visions of the traditional schoolyard bully and the subordinate victim. However, bullying is no longer limited to in-person encounter, having come to include cyberbullying, which takes place indirectly over electronic media. In this electronic age, cyber platforms proliferate at an astonishing rate, all attracting the youth in large number, and posing the risk that they may become subject to cyberbullying. Far from being limited to those individual learners being cyberbullied, the effects of this phenomenon extend to the learner collective, the school climate, and also the entire school system, management and education, thus requiring an urgent response. This article first provides a general overview of cyberbullying and its impact on learners, schools and education. This is done through a comparative lens, studying the extent of the phenomenon in both the United States and South Africa. The focus then shifts to the existing legislative frameworks within which the phenomenon is tackled in these respective jurisdictions, particularly the tricky balancing act required between learners’ constitutional right to free speech and expression, and the protection of vulnerable learners’ right to equality, dignity and privacy. The article concludes by proposing certain possible solutions to the problem.

Author(s): Rachoene, M., & Oyedemi, T.

Title: From self-expression to social aggression: Cyberbullying culture among South African youth on Facebook.

Journal: Communicatio

URL: www.tandfonline.com/doi/pdf/10.1080/02500167.2015.1093325

Abstract: Social media platforms propagate a culture of self-expression by empowering individuals to create, control and broadcast own content. Social networking sites are particularly popular tools for the youth’s self-expressive practices; hence, the concerns about how these tools are used, and their implications for culture and sociability. One of these concerns is the culture of cyberbullying. This study examines online bullying among South African youth on Facebook. A pattern of cyberbullying among the youth was identified through a non-participatory digital ethnography that involved daily observations and a study of postings and comments on six Facebook pages that university students and township youth subscribed to. The study revealed that attacks on intelligence and physical appearance, sexting and outing, insults and threats are common bullying types. However, sexting and outing with the use of sexually explicit pictures is very common among this population. In a digital culture where privacy is becoming more lax and visibility embedded in a self-expressive culture is celebrated, there is a concern about the consequences of this culture, particularly for the victims of cyberbullying.

Qualitative Methods in School Bullying and Cyberbullying Research: An Introduction to the Special Issue

• Published: 12 August 2022
• Volume 4 , pages 175–179, ( 2022 )

Cite this article

• Paul Horton 1 &
• Selma Therese Lyng 2  

9375 Accesses

12 Altmetric

Explore all metrics

Avoid common mistakes on your manuscript.

Introduction

School bullying research has a long history, stretching all the way back to a questionnaire study undertaken in the USA in the late 1800s (Burk, 1897 ). However, systematic school bullying research began in earnest in Scandinavia in the early 1970s with the work of Heinemann ( 1972 ) and Olweus ( 1978 ). Highlighting the extent to which research on bullying has grown exponentially since then, Smith et al. ( 2021 ) found that there were only 83 articles with the term “bully” in the title or abstract published in the Web of Science database prior to 1989. The numbers of articles found in the following decades were 458 (1990–1999), 1,996 (2000–2009), and 9,333 (2010–2019). Considering cyberbullying more specifically, Smith and Berkkun ( 2017 , cited in Smith et al., 2021 ) conducted a search of Web of Science with the terms “cyber* and bully*; cyber and victim*; electronic bullying; Internet bullying; and online harassment” until the year 2015 and found that while there were no articles published prior to 2000, 538 articles were published between 2000 and 2015, with the number of articles increasing every year (p. 49).

Numerous authors have pointed out that research into school bullying and cyberbullying has predominantly been conducted using quantitative methods, with much less use of qualitative or mixed methods (Hong & Espelage, 2012 ; Hutson, 2018 ; Maran & Begotti, 2021 ; Smith et al., 2021 ). In their recent analysis of articles published between 1976 and 2019 (in WoS, with the search terms “bully*; victim*; cyberbullying; electronic bullying; internet bullying; and online harassment”), Smith et al. ( 2021 , pp. 50–51) found that of the empirical articles selected, more than three-quarters (76.3%) were based on quantitative data, 15.4% were based on a combination of quantitative and qualitative data, and less than one-tenth (8.4%) were based on qualitative data alone. What is more, they found that the proportion of articles based on qualitative or mixed methods has been decreasing over the past 15 years (Smith et al., 2021 ). While the search criteria excluded certain types of qualitative studies (e.g., those published in books, doctoral theses, and non-English languages), this nonetheless highlights the extent to which qualitative research findings risk being overlooked in the vast sea of quantitative research.

School bullying and cyberbullying are complex phenomena, and a range of methodological approaches is thus needed to understand their complexity (Pellegrini & Bartini, 2000 ; Thornberg, 2011 ). Indeed, over-relying on quantitative methods limits understanding of the contexts and experiences of bullying (Hong & Espelage, 2012 ; Patton et al., 2017 ). Qualitative methods are particularly useful for better understanding the social contexts, processes, interactions, experiences, motivations, and perspectives of those involved (Hutson, 2018 ; Patton et al., 2017 ; Thornberg, 2011 ; Torrance, 2000 ).

Smith et al. ( 2021 ) suggest that the “continued emphasis on quantitative studies may be due to increasingly sophisticated methods such as structural equation modeling … network analysis … time trend analyses … latent profile analyses … and multi-polygenic score approaches” (p. 56). However, the authors make no mention of the range or sophistication of methods used in qualitative studies. Although there are still proportionately few qualitative studies of school bullying and cyberbullying in relation to quantitative studies, and this gap appears to be increasing, qualitative studies have utilized a range of qualitative data collection methods. These methods have included but are not limited to ethnographic fieldwork and participant observations (e.g., Eriksen & Lyng, 2018 ; Gumpel et al., 2014 ; Horton, 2019 ), digital ethnography (e.g., Rachoene & Oyedemi, 2015 ; Sylwander, 2019 ), meta-ethnography (e.g., Dennehy et al., 2020 ; Moretti & Herkovits, 2021 ), focus group interviews (e.g., Odenbring, 2022 ; Oliver & Candappa, 2007 ; Ybarra et al., 2019 ), semi-structured group and individual interviews (e.g., Forsberg & Thornberg, 2016 ; Lyng, 2018 ; Mishna et al., 2005 ; Varjas et al., 2013 ), vignettes (e.g., Jennifer & Cowie, 2012 ; Khanolainen & Semenova, 2020 ; Strindberg et al., 2020 ), memory work (e.g., Johnson et al., 2014 ; Malaby, 2009 ), literature studies (e.g., Lopez-Ropero, 2012 ; Wiseman et al., 2019 ), photo elicitation (e.g., Ganbaatar et al., 2021 ; Newman et al., 2006 ; Walton & Niblett, 2013 ), photostory method (e.g., Skrzypiec et al., 2015 ), and other visual works produced by children and young people (e.g., Bosacki et al., 2006 ; Gillies-Rezo & Bosacki, 2003 ).

This body of research has also included a variety of qualitative data analysis methods, such as grounded theory (e.g., Allen, 2015 ; Bjereld, 2018 ; Thornberg, 2018 ), thematic analysis (e.g., Cunningham et al., 2016 ; Forsberg & Horton, 2022 ), content analysis (e.g., Temko, 2019 ; Wiseman & Jones, 2018 ), conversation analysis (e.g., Evaldsson & Svahn, 2012 ; Tholander, 2019 ), narrative analysis (e.g., Haines-Saah et al., 2018 ), interpretative phenomenological analysis (e.g., Hutchinson, 2012 ; Tholander et al., 2020 ), various forms of discourse analysis (e.g., Ellwood & Davies, 2010 ; Hepburn, 1997 ; Ringrose & Renold, 2010 ), including discursive psychological analysis (e.g., Clarke et al., 2004 ), and critical discourse analysis (e.g., Barrett & Bound, 2015 ; Bethune & Gonick, 2017 ; Horton, 2021 ), as well as theoretically informed analyses from an array of research traditions (e.g., Davies, 2011 ; Jacobson, 2010 ; Søndergaard, 2012 ; Walton, 2005 ).

In light of the growing volume and variety of qualitative studies during the past two decades, we invited researchers to discuss and explore methodological issues related to their qualitative school bullying and cyberbullying research. The articles included in this special issue of the International Journal of Bullying Prevention discuss different qualitative methods, reflect on strengths and limitations — possibilities and challenges, and suggest implications for future qualitative and mixed-methods research.

Included Articles

Qualitative studies — focusing on social, relational, contextual, processual, structural, and/or societal factors and mechanisms — have formed the basis for several contributions during the last two decades that have sought to expand approaches to understanding and theorizing the causes of cyber/bullying. Some have also argued the need for expanding the commonly used definition of bullying, based on Olweus ( 1993 ) (e.g., Allen, 2015 ; Ellwood & Davies, 2010 Goldsmid & Howie, 2014 ; Ringrose & Rawlings,  2015 ; Søndergaard, 2012 ; Walton, 2011 ). In the first article of the special issue, Using qualitative methods to measure and understand key features of adolescent bullying: A call to action , Natalie Spadafora, Anthony Volk, and Andrew Dane instead discuss the usefulness of qualitative methods for improving measures and bettering our understanding of three specific key definitional features of bullying. Focusing on the definition put forward by Volk et al. ( 2014 ), they discuss the definitional features of power imbalance , goal directedness (replacing “intent to harm” in order not to assume conscious awareness, and to include a wide spectrum of goals that are intentionally and strategically pursued by bullies), and harmful impact (replacing “negative actions” in order to focus on the consequences for the victim, as well as circumventing difficult issues related to “repetition” in the traditional definition).

Acknowledging that these three features are challenging to capture using quantitative methods, Spadafora, Volk, and Dane point to existing qualitative studies that shed light on the features of power imbalance, goal directedness and harmful impact in bullying interactions — and put forward suggestions for future qualitative studies. More specifically, the authors argue that qualitative methods, such as focus groups, can be used to investigate the complexity of power relations at not only individual, but also social levels. They also highlight how qualitative methods, such as diaries and autoethnography, may help researchers gain a better understanding of the motives behind bullying behavior; from the perspectives of those engaging in it. Finally, the authors demonstrate how qualitative methods, such as ethnographic fieldwork and semi-structured interviews, can provide important insights into the harmful impact of bullying and how, for example, perceived harmfulness may be connected to perceived intention.

In the second article, Understanding bullying and cyberbullying through an ecological systems framework: The value of qualitative interviewing in a mixed methods approach , Faye Mishna, Arija Birze, and Andrea Greenblatt discuss the ways in which utilizing qualitative interviewing in mixed method approaches can facilitate greater understanding of bullying and cyberbullying. Based on a longitudinal and multi-perspective mixed methods study of cyberbullying, the authors demonstrate not only how qualitative interviewing can augment quantitative findings by examining process, context and meaning for those involved, but also how qualitative interviewing can lead to new insights and new areas of research. They also show how qualitative interviewing can help to capture nuances and complexity by allowing young people to express their perspectives and elaborate on their answers to questions. In line with this, the authors also raise the importance of qualitative interviewing for providing young people with space for self-reflection and learning.

In the third article, Q methodology as an innovative addition to bullying researchers’ methodological repertoire , Adrian Lundberg and Lisa Hellström focus on Q methodology as an inherently mixed methods approach, producing quantitative data from subjective viewpoints, and thus supplementing more mainstream quantitative and qualitative approaches. The authors outline and exemplify Q methodology as a research technique, focusing on the central feature of Q sorting. The authors further discuss the contribution of Q methodology to bullying research, highlighting the potential of Q methodology to address challenges related to gaining the perspectives of hard-to-reach populations who may either be unwilling or unable to share their personal experiences of bullying. As the authors point out, the use of card sorting activities allows participants to put forward their subjective perspectives, in less-intrusive settings for data collection and without disclosing their own personal experiences. The authors also illustrate how the flexibility of Q sorting can facilitate the participation of participants with limited verbal literacy and/or cognitive function through the use of images, objects or symbols. In the final part of the paper, Lundberg and Hellström discuss implications for practice and suggest future directions for using Q methodology in bullying and cyberbullying research, particularly with hard-to-reach populations.

In the fourth article, The importance of being attentive to social processes in school bullying research: Adopting a constructivist grounded theory approach , Camilla Forsberg discusses the use of constructivist grounded theory (CGT) in her research, focusing on social structures, norms, and processes. Forsberg first outlines CGT as a theory-methods package that is well suited to meet the call for more qualitative research on participants’ experiences and the social processes involved in school bullying. Forsberg emphasizes three key focal aspects of CGT, namely focus on participants’ main concerns; focus on meaning, actions, and processes; and focus on symbolic interactionism. She then provides examples and reflections from her own ethnographic and interview-based research, from different stages of the research process. In the last part of the article, Forsberg argues that prioritizing the perspectives of participants is an ethical stance, but one which comes with a number of ethical challenges, and points to ways in which CGT is helpful in dealing with these challenges.

In the fifth article, A qualitative meta-study of youth voice and co-participatory research practices: Informing cyber/bullying research methodologies , Deborah Green, Carmel Taddeo, Deborah Price, Foteini Pasenidou, and Barbara Spears discuss how qualitative meta-studies can be used to inform research methodologies for studying school bullying and cyberbullying. Drawing on the findings of five previous qualitative studies, and with a transdisciplinary and transformative approach, the authors illustrate and exemplify how previous qualitative research can be analyzed to gain a better understanding of the studies’ collective strengths and thus consider the findings and methods beyond the original settings where the research was conducted. In doing so, the authors highlight the progression of youth voice and co-participatory research practices, the centrality of children and young people to the research process and the enabling effect of technology — and discuss challenges related to ethical issues, resource and time demands, the role of gatekeepers, and common limitations of qualitative studies on youth voice and co-participatory research practices.

Taken together, the five articles illustrate the diversity of qualitative methods used to study school bullying and cyberbullying and highlight the need for further qualitative research. We hope that readers will find the collection of articles engaging and that the special issue not only gives impetus to increased qualitative focus on the complex phenomena of school bullying and cyberbullying but also to further discussions on both methodological and analytical approaches.

Allen, K. A. (2015). “We don’t have bullying, but we have drama”: Understandings of bullying and related constructs within the school milieu of a U.S. high school. Journal of Human Behavior in the Social Environment , 25 (3), 159–181.

Barrett, B., & Bound, A. M. (2015). A critical discourse analysis of No Promo Homo policies in US schools. Educational Studies, 51 (4), 267–283.

Article   Google Scholar  

Bethune, J., & Gonick, M. (2017). Schooling the mean girl: A critical discourse analysis of teacher resource materials. Gender and Education, 29 (3), 389–404.

Bjereld, Y. (2018). The challenging process of disclosing bullying victimization: A grounded theory study from the victim’s point of view. Journal of Health Psychology, 23 (8), 1110–1118.

Article   PubMed   Google Scholar  

Bosacki, S. L., Marini, Z. A., & Dane, A. V. (2006). Voices from the classroom: Pictorial and narrative representations of children’s bullying experiences. Journal of Moral Education, 35 (2), 231–245.

Burk, F. L. (1897). Teasing and Bullying. Pedagogical Seminary, 4 (3), 336–371.

Clarke, V., Kitzinger, C., & Potter, J. (2004). ‘Kids are just cruel anyway’: Lesbian and gay parents’ talk about homophobic bullying. British Journal of Social Psychology, 43 (4), 531–550.

Cunningham, C. E., Mapp, C., Rimas, H., Cunningham, S. M., Vaillancourt, T., & Marcus, M. (2016). What limits the effectiveness of antibullying programs? A thematic analysis of the perspective of students. Psychology of Violence, 6 (4), 596–606.

Davies, B. (2011). Bullies as guardians of the moral order or an ethic of truths? Children & Society, 25 , 278–286.

Dennehy, R., Meaney, S., Walsh, K. A., Sinnott, C., Cronin, M., & Arensman, E. (2020). Young people’s conceptualizations of the nature of cyberbullying: A systematic review and synthesis of qualitative research. Aggression and Violent Behavior, 51 , 101379.

Ellwood, C., & Davies, B. (2010). Violence and the moral order in contemporary schooling: A discursive analysis. Qualitative Research in Psychology, 7 (2), 85–98.

Eriksen, I. M., & Lyng, S. T. (2018). Relational aggression among boys: Blind spots and hidden dramas. Gender and Education, 30 (3), 396–409.

Evaldsson, A. -C., Svahn, J. (2012). School bullying and the micro-politics of girls’ gossip disputes. In S. Danby & M. Theobald (Eds.). Disputes in everyday life: Social and moral orders of children and young people (Sociological Studies of Children and Youth, Vol. 15) (pp. 297–323). Bingley: Emerald Group Publishing.

Forsberg, C., & Horton, P. (2022). ‘Because I am me’: School bullying and the presentation of self in everyday school life. Journal of Youth Studies, 25 (2), 136–150.

Forsberg, C., & Thornberg, R. (2016). The social ordering of belonging: Children’s perspectives on bullying. International Journal of Educational Research, 78 , 13–23.

Ganbaatar, D., Vaughan, C., Akter, S., & Bohren, M. A. (2021). Exploring the identities and experiences of young queer people in Mongolia using visual research methods. Culture, Health & Sexuality . Advance Online Publication: https://doi.org/10.1080/13691058.2021.1998631

Gillies-Rezo, S., & Bosacki, S. (2003). Invisible bruises: Kindergartners’ perceptions of bullying. International Journal of Children’s Spirituality, 8 (2), 163–177.

Goldsmid, S., & Howie, P. (2014). Bullying by definition: An examination of definitional components of bullying. Emotional and Behavioural Difficulties, 19 (2), 210–225.

Gumpel, T. P., Zioni-Koren, V., & Bekerman, Z. (2014). An ethnographic study of participant roles in school bullying. Aggressive Behavior, 40 (3), 214–228.

Haines-Saah, R. J., Hilario, C. T., Jenkins, E. K., Ng, C. K. Y., & Johnson, J. L. (2018). Understanding adolescent narratives about “bullying” through an intersectional lens: Implications for youth mental health interventions. Youth & Society, 50 (5), 636–658.

Heinemann, P. -P. (1972). Mobbning – gruppvåld bland barn och vuxna [Bullying – group violence amongst children and adults]. Stockholm: Natur och Kultur.

Hepburn, A. (1997). Discursive strategies in bullying talk. Education and Society, 15 (1), 13–31.

Hong, J. S., & Espelage, D. L. (2012). A review of mixed methods research on bullying and peer victimization in school. Educational Review, 64 (1), 115–126.

Horton, P. (2019). The bullied boy: Masculinity, embodiment, and the gendered social-ecology of Vietnamese school bullying. Gender and Education, 31 (3), 394–407.

Horton, P. (2021). Building walls: Trump election rhetoric, bullying and harassment in US schools. Confero: Essays on Education, Philosophy and Politics , 8 (1), 7–32.

Hutchinson, M. (2012). Exploring the impact of bullying on young bystanders. Educational Psychology in Practice, 28 (4), 425–442.

Hutson, E. (2018). Integrative review of qualitative research on the emotional experience of bullying victimization in youth. The Journal of School Nursing, 34 (1), 51–59.

Jacobson, R. B. (2010). A place to stand: Intersubjectivity and the desire to dominate. Studies in Philosophy and Education, 29 , 35–51.

Jennifer, D., & Cowie, H. (2012). Listening to children’s voices: Moral emotional attributions in relation to primary school bullying. Emotional and Behavioural Difficulties, 17 (3–4), 229–241.

Johnson, C. W., Singh, A. A., & Gonzalez, M. (2014). “It’s complicated”: Collective memories of transgender, queer, and questioning youth in high school. Journal of Homosexuality, 61 (3), 419–434.

Khanolainen, D., & Semenova, E. (2020). School bullying through graphic vignettes: Developing a new arts-based method to study a sensitive topic. International Journal of Qualitative Methods, 19 , 1–15.

Lopez-Ropero, L. (2012). ‘You are a flaw in the pattern’: Difference, autonomy and bullying in YA fiction. Children’s Literature in Education, 43 , 145–157.

Lyng, S. T. (2018). The social production of bullying: Expanding the repertoire of approaches to group dynamics. Children & Society, 32 (6), 492–502.

Malaby, M. (2009). Public and secret agents: Personal power and reflective agency in male memories of childhood violence and bullying. Gender and Education, 21 (4), 371–386.

Maran, D. A., & Begotti, T. (2021). Measurement issues relevant to qualitative studies. In P. K. Smith & J. O’Higgins Norman (Eds.). The Wiley handbook of bullying (pp. 233–249). John Wiley & Sons.

Mishna, F., Scarcello, I., Pepler, D., & Wiener, J. (2005). Teachers’ understandings of bullying. Canadian Journal of Education, 28 (4), 718–738.

Moretti, C., & Herkovits, D. (2021). Victims, perpetrators, and bystanders: A meta-ethnography of roles in cyberbullying. Cad. Saúde Pública, 37 (4), e00097120.

Newman, M., Woodcock, A., & Dunham, P. (2006). ‘Playtime in the borderlands’: Children’s representations of school, gender and bullying through photographs and interviews. Children’s Geographies, 4 (3), 289–302.

Odenbring, Y. (2022). Standing alone: Sexual minority status and victimisation in a rural lower secondary school. International Journal of Inclusive Education, 26 (5), 480–494.

Oliver, C., & Candappa, M. (2007). Bullying and the politics of ‘telling.’ Oxford Review of Education, 33 (1), 71–86.

Olweus, D. (1978). Aggression in the schools – Bullies and the whipping boys . Wiley.

Google Scholar  

Olweus, D. (1993). Bullying in school: What we know and what we can do . Blackwell.

Patton, D. U., Hong, J. S., Patel, S., & Kral, M. J. (2017). A systematic review of research strategies used in qualitative studies on school bullying and victimization. Trauma, Violence, & Abuse, 18 (1), 3–16.

Pellegrini, A. D., & Bartini, M. (2000). A longitudinal study of bullying, victimization, and peer affiliation during the transition from primary school to middle school. American Educational Research Journal, 37 (3), 699–725.

Rachoene, M., & Oyedemi, T. (2015). From self-expression to social aggression: Cyberbullying culture among South African youth on Facebook. Communicatio: South African Journal for Communication Theory and Research , 41 (3), 302–319.

Ringrose, J., & Rawlings, V. (2015). Posthuman performativity, gender and ‘school bullying’: Exploring the material-discursive intra-actions of skirts, hair, sluts, and poofs.  Confero: Essays on Education, Philosophy and Politics , 3 (2), 80–119.

Ringrose, J., & Renold, E. (2010). Normative cruelties and gender deviants: The performative effects of bully discourses for girls and boys in school. British Educational Research Journal, 36 (4), 573–596.

Skrzypiec, G., Slee, P., & Sandhu, D. (2015). Using the PhotoStory method to understand the cultural context of youth victimization in the Punjab. The International Journal of Emotional Education, 7 (1), 52–68.

Smith, P., Robinson, S., & Slonje, R. (2021). The school bullying research program: Why and how it has developed. In P. K. Smith & J. O’Higgins Norman (Eds.). The Wiley handbook of bullying (pp. 42–59). John Wiley & Sons.

Smith, P. K., & Berkkun, F. (2017). How research on school bullying has developed. In C. McGuckin & L. Corcoran (Eds.), Bullying and cyberbullying: Prevalence, psychological impacts and intervention strategies (pp. 11–27). Hauppage, NY: Nova Science.

Strindberg, J., Horton, P., & Thornberg, R. (2020). The fear of being singled out: Pupils’ perspectives on victimization and bystanding in bullying situations. British Journal of Sociology of Education, 41 (7), 942–957.

Sylwander, K. R. (2019). Affective atmospheres of sexualized hate among youth online: A contribution to bullying and cyberbullying research on social atmosphere. International Journal of Bullying Prevention, 1 , 269–284.

Søndergaard, D. M. (2012). Bullying and social exclusion anxiety in schools. British Journal of Sociology of Education, 33 (3), 355–372.

Temko, E. (2019). Missing structure: A critical content analysis of the Olweus Bullying Prevention Program. Children & Society, 33 (1), 1–12.

Tholander, M. (2019). The making and unmaking of a bullying victim. Interchange, 50 , 1–23.

Tholander, M., Lindberg, A., & Svensson, D. (2020). “A freak that no one can love”: Difficult knowledge in testimonials on school bullying. Research Papers in Education, 35 (3), 359–377.

Thornberg, R. (2011). ‘She’s weird!’ – The social construction of bullying in school: A review of qualitative research. Children & Society, 25 , 258–267.

Thornberg, R. (2018). School bullying and fitting into the peer landscape: A grounded theory field study. British Journal of Sociology of Education, 39 (1), 144–158.

Torrance, D. A. (2000). Qualitative studies into bullying within special schools. British Journal of Special Education, 27 (1), 16–21.

Varjas, K., Meyers, J., Kiperman, S., & Howard, A. (2013). Technology hurts? Lesbian, gay, and bisexual youth perspectives of technology and cyberbullying. Journal of School Violence, 12 (1), 27–44.

Volk, A. A., Dane, A. V., & Marini, Z. A. (2014). What is bullying? A Theoretical Redefinition, Developmental Review, 34 (4), 327–343.

Walton, G. (2005). Bullying widespread. Journal of School Violence, 4 (1), 91–118.

Walton, G. (2011). Spinning our wheels: Reconceptualizing bullying beyond behaviour-focused Approaches.  Discourse: Studies in the Cultural Politics of Education , 32 (1), 131–144.

Walton, G., & Niblett, B. (2013). Investigating the problem of bullying through photo elicitation. Journal of Youth Studies, 16 (5), 646–662.

Wiseman, A. M., & Jones, J. S. (2018). Examining depictions of bullying in children’s picturebooks: A content analysis from 1997 to 2017. Journal of Research in Childhood Education, 32 (2), 190–201.

Wiseman, A. M., Vehabovic, N., & Jones, J. S. (2019). Intersections of race and bullying in children’s literature: Transitions, racism, and counternarratives. Early Childhood Education Journal, 47 , 465–474.

Ybarra, M. L., Espelage, D. L., Valido, A., Hong, J. S., & Prescott, T. L. (2019). Perceptions of middle school youth about school bullying. Journal of Adolescence, 75 , 175–187.

Download references

Acknowledgements

We would like to thank the authors for sharing their work; Angela Mazzone, James O’Higgins Norman, and Sameer Hinduja for their editorial assistance; and Dorte Marie Søndergaard on the editorial board for suggesting a special issue on qualitative research in the journal.

Author information

Authors and affiliations.

Department of Behavioural Sciences and Learning (IBL), Linköping University, Linköping, Sweden

Paul Horton

Work Research Institute (WRI), Oslo Metropolitan University, Oslo, Norway

Selma Therese Lyng

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Paul Horton .

Rights and permissions

Reprints and permissions

About this article

Horton, P., Lyng, S.T. Qualitative Methods in School Bullying and Cyberbullying Research: An Introduction to the Special Issue. Int Journal of Bullying Prevention 4 , 175–179 (2022). https://doi.org/10.1007/s42380-022-00139-5

Download citation

Published : 12 August 2022

Issue Date : September 2022

DOI : https://doi.org/10.1007/s42380-022-00139-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Find a journal
• Publish with us
• Track your research

• Connect with us:
• X (Twitter)
• CAS Newsletter | CASNews | Redbird Scholar

Research Challenge Grant winners announced

• Author By Deborah Fox
• April 26, 2024

Eight research projects involving 16 faculty members are the winners of the inaugural Dean’s Research Challenge Grants. Proposals submitted this year were required to focus on one of two themes: “Equity” or “Environment.”

Established in 2023 to encourage innovative scholarly work, the College of Arts and Sciences committed $47,000 to this initiative.

“Supporting faculty research is a top priority,” said Dr. Heather Dillaway, dean of the College of Arts and Sciences. “In the College of Arts and Sciences, we view the University’s motto of ‘Gladly we learn and teach,’ as being directly intertwined with research excellence. The purpose of universities, in the purest sense, is to create knowledge and share knowledge. That means we must invest in both research and teaching.”

2023-2024 winning proposals

Awards: equity.

• Michael Hendricks (POL) along with co-PIs Noha Shawki (POL), Joan Brehm (SOA), and Eric Peterson (GEO) – Cleaning up the river without clearing out the neighborhood: Floating gardens in the Chicago River
• Dan Ispas (PSY) along with co-PI Alexandra Ilie (PSY) – Exploring equity in personnel selection: Investigating differential prediction and applicant reactions to personality tests
• Maura Toro-Morn (SOA-LALS) along with co-PI Jim Pancrazio (LAN) – project aims to assess and capture the experiences of COBAS program participants
• Susan Chen (ECO) – gender and race disparities in the impact of COVID-19 on job market outcomes

Awards: Environment

• Ben Wodika (BIO) along with co-PIs Vickie Borowicz , Matt Dugas , and Sydney Metternich – Sugar Creek Urban Ecology Area Committee Application: Do species matter? A test of the Functional Equivalence Hypothesis using Ground Beetles
• Christopher Mulligan (CHE) – Next Generation analytical methods for on-site and on-demand environmental pollutant monitoring
• Sudipa Topdar (HIS) – Lifeless Feathers, Masculinity, and Ecological Imperialism in the Late Colonial Himalayas (1800-1947)
• Maochao Xu (MAT) – Cyberbullying Dynamics: A Predictive modeling exploration in college communities

Award recipients will deliver presentations about their research projects at a College of Arts and Sciences Research Symposium on Friday, October 11. Additional details about this event will be forthcoming.

Related Articles

• Privacy Statement
• Appropriate Use Policy
• Accessibility Resources

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Child Adolesc Psychiatry Ment Health

Bullying in school and cyberspace: Associations with depressive symptoms in Swiss and Australian adolescents

Sonja perren.

1 Jacobs Center for Productive Youth Development, University of Zürich, Culmannstrasse 1, 8001 Zürich, Switzerland

Julian Dooley

2 Child Health Promotion Research Centre, Edith Cowan University, WA, Australia

Thérèse Shaw

Donna cross.

Cyber-bullying (i.e., bullying via electronic means) has emerged as a new form of bullying that presents unique challenges to those victimised. Recent studies have demonstrated that there is a significant conceptual and practical overlap between both types of bullying such that most young people who are cyber-bullied also tend to be bullied by more traditional methods. Despite the overlap between traditional and cyber forms of bullying, it remains unclear if being a victim of cyber-bullying has the same negative consequences as being a victim of traditional bullying.

The current study investigated associations between cyber versus traditional bullying and depressive symptoms in 374 and 1320 students from Switzerland and Australia respectively (52% female; Age: M = 13.8, SD = 1.0). All participants completed a bullying questionnaire (assessing perpetration and victimisation of traditional and cyber forms of bullying behaviour) in addition to scales on depressive symptoms.

Across both samples, traditional victims and bully-victims reported more depressive symptoms than bullies and non-involved children. Importantly, victims of cyber-bullying reported significantly higher levels of depressive symptoms, even when controlling for the involvement in traditional bullying/victimisation.

Conclusions

Overall, cyber-victimisation emerged as an additional risk factor for depressive symptoms in adolescents involved in bullying.

It is well established that students who are bullied by their peers are at higher risk for internalizing problems. Recently, a new form of bullying behaviour has come to the attention of school staff, clinicians, researchers and the general public, namely cyber-bullying. Although several definitions are proposed, cyber-bullying is generally considered to be bullying using technology such as the Internet and mobile phones [ 1 - 3 ]. Recent studies have demonstrated that there is a significant conceptual and practical overlap between both types of bullying such that most young people who are cyber-bullied also tend to be bullied by more traditional methods [ 4 - 6 ]. Despite the overlap between traditional and cyber forms of bullying, it remains unclear if being a victim of cyber-bullying has the same negative consequences as being a victim of traditional bullying. Therefore, to investigate this we differentiate between two types of bullying: traditional bullying , including physical or verbal harassment, exclusion, relational aggression and cyber-bullying , involving the use of some kind of electronic media (i.e., Internet or mobile phone) to engage in bullying behaviour. The aim of the current study was to investigate the associations between both types of bullying and depressive symptoms in adolescents from two different countries.

Consequences and correlates of peer victimisation

As children develop, the peer context acquires increasing importance for health and well-being [ 7 ]. Peer problems during childhood and adolescence can often result in disruptions to healthy functioning both for those who engage in disruptive behaviours as well as those who are victimised.

It is well established that being a victim of bullying has negative short- and long-term consequences. Furthermore, it is reported that negative peer relations such as lack of acceptance in the peer group and peer victimisation are associated with loneliness, social dissatisfaction and social withdrawal [ 8 ] and emotional and behavioural symptoms [ 9 ]. Importantly, evidence from several longitudinal studies has demonstrated that peer victimisation and exclusion may also increase children's depressive symptoms [ 10 - 13 ]. These findings indicate that peer rejection and victimisation may play a causal role in the development of depressive symptoms. Consistently, the causal influence of peer victimisation on symptoms of depression was supported by the results of a recent twin study [ 14 ].

A meta-analytic review of cross-sectional associations between peer victimisation and psychosocial maladjustment provided clear evidence that peer victimisation is most strongly related to symptoms of depression and least strongly to anxiety [ 15 ]. Peer victimisation is also associated with low self-esteem, health problems, suicidality, and poor school adjustment [ 16 - 20 ].

Consequences and correlates of bullying behaviours

Young people who bully others also often experience negative consequences related to their behaviour, some of which are not immediately apparent [ 21 ]. For example, primary and middle school students who bully others often seem unscathed, as their social standing and self-concept are similar to that of observers and markedly better than those who are bullied. Early on, these young people are seen as positive leaders with a good sense of humour, high self-esteem qualities and positive early friendship qualities and popularity [ 22 , 23 ].

Nevertheless, as children grow older bullying behaviours become increasingly maladaptive. Whereas young children solve disputes by fighting, adolescents and adults prefer negotiation to solve a conflict [ 24 ]. Children who bully others often do not learn to interact and communicate in socially appropriate ways and therefore have difficulty in interacting adequately with their older peers. This often results in persistent maladaptive behavioural patterns [ 25 ], as well as representing an elevated risk for serious injury [ 26 ], alcohol dependency [ 27 ], and delinquency [ 28 ]. These findings suggest that children and adolescents who bully others, frequently also show other forms of antisocial behaviour and that some of those students show a pattern of life-course persistent antisocial behaviour [ 29 ].

Furthermore, adolescents who bully others are found to have more psychological and physical problems than their peers [ 30 ], and have an increased risk for depression and suicidal ideation [ 31 ]. Bullying research traditionally differentiates between children or adolescents who are only victims, only bullies or both [ 28 ]. Regarding potential outcomes of bullying, it has been shown that those who both bully others and are victimised (i.e. bully-victims) report the highest levels of externalizing and internalizing symptoms [ 31 , 32 ].

In sum, bullying perpetration and victimisation may have highly negative consequences for children's and adolescents' mental health and well-being. In general, bullying others is most strongly associated with externalizing problems, while being a victim of bullying is strongly associated with internalizing symptoms.

Consequences and correlates of cyber-bullying and cyber-victimisation

The existing (albeit limited) literature on cyber-bullying suggests that the consequences of cyber-bullying may be similar to traditional bullying. Cyber-bullying, like traditional bullying, correlates significantly with physical and psychological problems [ 33 ]. A large scale Australian-based bullying study also demonstrated that cyber-victimisation is associated with higher levels of stress symptoms [ 4 ]. Moreover, adolescent victims of cyber-bullying not only reported higher depressive symptoms but also that they engage in other types of problematic behaviour, such as increased alcohol consumption, a tendency to smoke and poor school grades [ 34 ]. Cross-sectional studies showed that aggressors are at increased risk for school problems, assaultive behaviours, and substance use [ 35 ]. These findings suggest that cyber-victimisation, like traditional victimisation, increases the risk of internalizing (and externalizing) problems.

However, as traditional and cyber-bullying forms are strongly associated and frequently co-occur within the same individuals [ 1 , 36 - 39 ] it is important to investigate both forms of bullying simultaneously. Few studies have systematically analysed the impact of cyber versus traditional bullying on adolescents' adjustment and mental health.

In a recent study with 761 adolescents from Austria the combined victim group (cyber and traditional victimisation) showed the highest level of internalizing problems [ 6 ]. In this study, combined bully-victims showed the most maladjusted pattern. Similarly, a Swedish study found that cyber-victimisation contributed over and above traditional victimisation to adolescents' social anxiety [ 40 ]. Cyber-victimisation is also associated with a range of negative emotions [ 41 ]. Qualitative data suggest that in comparison with traditional bullying forms, cyber-bullying evoked stronger negative feelings, fear and a clear sense of helplessness [ 42 ]. Therefore, being a victim of cyber-bullying might be even more strongly associated with depressive symptoms than traditional victimisation.

Research questions

This paper describes the relationship between traditional and cyber forms of bullying/victimisation and psychological outcomes. Several hypotheses were generated: (1) there is an overlap between traditional bullying/victimisation and cyber-bullying/victimisation; (2) traditional victims and bully-victims experience higher levels of depressive symptoms than those who bully others and non-involved students; and (3) cyber-victimisation represents an independent risk factor - over and above traditional victimisation - for higher levels of symptoms of depression.

In addition to the three main hypotheses, we examined the influence of culture on the relationship between perpetration/victimisation and outcome. Eslea and colleagues showed in a large dataset from seven different countries that victims of traditional bullying were significantly more disadvantaged on all measures (e.g., mental health, friendships) in all samples, whereas bullies did not differ consistently in all samples. The authors concluded that traditional bullying is a universal phenomenon with many negative correlates for victims and few (if any) for bullies [ 43 ]. The consequences associated with cyber-victimisation are not as well established as associations with traditional bullying/victimisation. Moreover, no cross-national comparison has been conducted regarding cyber-bullying so far. Given this, we investigated if the outcomes associated with traditional and cyber forms of bullying were similar for young people in Switzerland and Australia, i.e. we tested whether the results were replicated in both countries (Switzerland versus Australia).

Participants

Australia . Data for the Australian sample were taken from a cross-sectional study (the Cyber Friendly Schools study) to determine the prevalence of cyber-bullying behaviours in Western Australia (WA) conducted in 2008 by the Child Health Promotion Research Centre (CHPRC) at Edith Cowan University. Schools were randomly selected within strata defined by geographic location and school sector. Non-mainstream and smaller schools as well as those already involved in intervention projects conducted by the CHPRC were excluded, as were students with disabilities which prevented them from completing hard copy self-report surveys. Surveys were administered by school staff within classrooms to those students who consented to participate and for whom written consent was provided by their parents. The Australian students each received a small gift (less than a dollar in value) as thanks for participating in the study. Schools received a $50 voucher for a stationary/educational store and a report detailing study results. All students were provided with contact information for youth support agencies should they have experienced difficulties as a result of participating in the survey. The study was approved by the Edith Cowan University Human Research Ethics Committee.

To increase comparability between the two countries' data and due to different requirements for obtaining consent and subsequent low consent rates in government schools, only results from secondary non-government co-educational schools are reported below.

Relative to the schools included in these analyses, the parent consent rate was 94% with 73% of students returning completed usable questionnaires. Six percent of cases did not indicate gender on the questionnaire and are excluded from the analyses. A total of 22 participants did not indicate their age and those missing values were replaced with the mean age of their respective grade level. This sample comprised 1320 adolescents (Mean age = 13.7, SD = 0.92) from four religious-affiliated average socio-economic status schools (two metropolitan, two rural). The final sample was fairly evenly distributed between year levels (Australian Grade 8: 33.8%, Grade 9: 37.2%, Grade 10: 29.0%), by area (48.5% metropolitan) and by gender (52.8% female). Students' access to technology was high: 95% had access to the internet at home and about 92% had their own mobile phone.

Switzerland . Nineteen school classes (Grades 7 to 9 in the city of St. Gallen) participated in the study [ 44 ]. Schools and participating classrooms were selected to represent all city districts (Schulkreise) and to represent all three school types at the secondary level in Switzerland: Realschule with basic classes (low achievement level school, N = 7 classes), Sekundarschule with broader classes (average achievement level school, N = 6 classes) and Kantonschule with advanced classes (high achievement level school, N = 6 classes).

Following Swiss legislation, permission from the respective school councils to conduct the study was first obtained. Second, teachers from the selected schools volunteered. The survey procedure and the goal of the study were explained to the students who then had the opportunity to refrain from participation without negative consequences (informed oral consent). Students who did not want to participate were offered another activity during the respective school hour. Participating school classes received a voucher for books and media worth 50 Swiss Franks. Teachers and students received general feedback about the occurrence of bully/victim behaviours in their classes and an information flyer that provided contact information for students who may require help following completion of the survey.

Eight students were absent on the day of assessments and did not participate. Although no student actively refused to participate in the study, 6 questionnaires were not included in the study due to missing or incomplete information. The final study sample comprised 374 participants (53.2% female; mean age = 14.3 years, SD = 1.13). In total, 17 participants did not indicate their age and these missing values were replaced with the mean age of their respective school class. The sample was fairly evenly distributed between year levels: Swiss Grade 7: 31.8%, Grade 8: 31.8%, Grade 9: 36.6%. Half (51%) of participants reported a foreign-language or migration background, 28% spoke (Swiss) German and at least one other language at home and 23% did not speak (Swiss) German within their families. Students' access to technology was high: 97% had access to the internet at home and about 95% had their own mobile phone.

Assessment of traditional bullying and victimisation

In the following we differentiate between bullying (= perpetration) and victimisation (being a victim of bullying).

Australia . Participants reported on the frequency of traditional bullying and victimisation in the last 3 months (0 = never to 4 = most days this term). The 6 items address specific negative behaviours (was ignored/excluded; teased in nasty ways; physically hurt; frightened by what someone said they would do; hurtful rumours spread; property stolen, damaged or destroyed).

Switzerland . Participants reported on the frequency of traditional bullying and victimisation in the last 3 months (0 = never to 4 = several times a day). The 6 items were used to measure specific negative behaviours (verbal aggression, physical aggression, exclusion, indirect aggression, threat and property-related behaviours).

Both samples . Each of the 6 items described above were chosen from a larger item pool of items to make the assessments as similar as possible. Students' self-reports regarding the frequency of being a perpetrator or victim of different forms of traditional bullying were used for categorization into four mutually exclusive categories as bully-victims, victims, bullies, and non-involved students. The same cut-off was used in both samples (at least once a week on at least one item) to denote frequent bullying perpetration/victimisation.

Assessment of cyber-bullying and -victimisation

Australia . The frequency of cyber-bullying and cyber-victimisation were assessed in the same way as described for the traditional bullying (same time period and response options). Each scale encompassed 5 items (sent nasty or threatening emails, nasty messages on the Internet/to mobile phone and mean or nasty comments or pictures sent to websites/other students' mobile phones). Composite scores were calculated for the cyber-bullying behaviours by applying confirmatory factor analysis (see below).

Switzerland . Students also reported on the frequency of cyber-bullying and cyber-victimisation (same time period and response options as above). Each scale encompassed 2 items: being bullied through the use of mobile phones (calls, SMS, pictures, films); being bullied through the use of Internet (e-mail, social networking sites, chat). A mean score was computed to establish the scales.

Both samples . Due to the nature of cyber-bullying, repetition as a defining feature of this bullying behaviour may be hard to assess [ 5 ]. Therefore, no established cut-offs for being a cyber-bully or cyber-victim exist. In addition, dichotomising these scores would have led to an unnecessary loss of information with regard to various degrees of perpetration/victimisation. Thus, cyber-victimisation and cyber-bullying were analysed as linear variables. Whilst the response categories varied between the studies, this was mostly at the upper end of the scale where there were relatively few responses.

Assessment of depressive symptoms

Australia . Students completed a 14-item depression subscale of the Depression Anxiety Stress Scales (DASS) [ 45 ].

Switzerland . Students completed an 8-item scale addressing depressive symptoms. The scale has been validated in a longitudinal study [ 46 , 47 ].

Both samples . Both scales tap the same constructs: sad/depressed feelings, lack of positive feeling, lack of motivation/energy, worthlessness of life. Composite scores were calculated for the depressive symptoms by applying confirmatory factor analysis fitting a single-factor measurement model using weighted least squares estimation based on polychoric correlation matrices. This approach appropriately accounts for the skewed item distributions and measurement error in the items. To maximize data available for analyses, when 20% or less of the items were missing, values were imputed for the missing items based on observed items using the EM (expectation-maximization) algorithm prior to the factor analysis.

Data analyses

Data analyses accounted for the skew of the dependent variables through the use of tobit regressions, the data were log transformed to meet the requirement of normality of the non-censored scores as recommended by Osgood [ 48 ]. Our analyses also accounted for non-independence of the data resulting from the clustered sampling, which can lead to inflated Type I error rates, through the inclusion of a random intercept in the models. Clustering in the Australian data was by school (where secondary students within a year level move between classes for different subjects) and by class in the Swiss sample.

For the statistical analyses, a significance level of p < 0.05 was used.

Descriptive statistics

Table ​ Table1 1 shows means and standard deviations of all study variables by sample and gender.

Descriptive statistics of all study variables

a Numbers (percentages) of students within each country, (traditional bully-victim categories defined according to involvement in bullying behaviours once a week or more often in the past 3 months).

Traditional bully/victim categorization

Across both samples, students' self-reported frequency of traditional bullying perpetration/victimisation were used to categorize participants (cut-off: at least once a week): traditional victims (10.0%), bully-victim (3.6%), perpetrators (9.2%), and non-involved (77.2%). In addition, significant gender differences were found with more boys reporting they were frequently perpetrators (12.9%) than girls (5.9%), χ 2 = 31.1, N = 1666, p < .001. When country specific frequencies were examined (Table ​ (Table1), 1 ), significantly more Swiss participants reported bullying others than did their Australian counterparts (14.5% versus 7.7%), χ 2 = 20.9, N = 1666, p < .001.

Country and gender differences regarding the other variables are reported in the multivariable analyses below.

Bivariate associations

Both types of bullying and victimisation were significantly associated with each other (see Table ​ Table2 2 and Table ​ Table3). 3 ). These relationships remained statistically significant (all p < .01) when examined by country, with stronger associations observed in the Australian sample. When comparing the traditional bully-victim categories, 41% of (traditional) bullies, 59% of bully-victims, 30% of victims and 16% of non-involved students reported perpetrating cyber-bullying behaviours at least once or twice. Thirty-nine percent of (traditional) victims, 50% of bully-victims, 22% of bullies and 17% of non-involved students were exposed to cyber-bullying behaviours at least once or twice. The association between bullying behaviour and mental health revealed some interesting results with depressive symptoms being most strongly correlated with traditional victimisation (Spearman's rho = .26 Australian sample, rho = .24 Swiss sample) and cyber-victimisation (rho = .22 Australian sample, rho = .12 Swiss sample).

Bivariate associations between study variables: Complete sample

Note: Spearman's rho calculated for correlations involving cyber-victimization, cyber-bullying and depressive symptoms, Pearson's correlation calculated for all others

*p < .05, **p < .01

Bivariate associations between study variables: Australian versus Swiss sample

Note: Spearman's rho calculated for correlations involving cyber-victimisation, cyber-bullying and depressive symptoms, Pearson's correlation calculated for all others

*p < .05, **p < .01 two sided tests

Overlap of bullying/victimisation forms: Multivariable analyses

Next, two tobit regression analyses were conducted to analyse differences between those who use traditional methods to bully, those who are victimised, the combined group (hereafter bully-victims for brevity) and non-involved students in terms of their tendency to cyber-bully others and be cyber-victimised (as log-transformed linear dependent variables). Age and gender and country were entered as control variables. As we were interested in whether country moderates the associations, location (i.e., Switzerland or Australia) was entered as an interaction effect in a first model.

Cyber-victimisation

The bully/victim categorization interaction effect with country was found not to be significant (χ 2 [3]= 6.3, p = .098) and was thus dropped from the model The subsequent analysis yielded significant main effects for the bully/victim categorization, gender and country (see Table ​ Table4). 4 ). As is evidenced by the positive sign for the Z statistic, girls reported higher levels of cyber-victimisation than boys (z = 4.75, p < .001). The Australian students reported being more frequently cyber-victimised than the Swiss students (z = 4.46, p < .001). All of the traditional bully/victim behaviour categories differed significantly from each other (see also Table ​ Table5). 5 ). Bully-victims and victims reported higher levels of cyber-victimisation than non-involved students and bullies, of these victims had lower scores on cyber-victimisation than the bully-victims. Students who indicated they bullied others by traditional means reported higher levels of being cyber-victimised than those non-involved in traditional bullying behaviours.

Results of the tobit regression predicting cyber-victimisation and cyber-bullying

Note: Cyber-victimisation: R 2 = 14.0%; Cyber-bullying: R 2 = 16.5%; Depressive symptoms (M1): R 2 = 12.8%; Depressive symptoms (M2): R 2 = 16.1%

Summary statistics for cyber-victimisation, cyber-bullying and depressive symptoms by traditional bully/victim categorization

Cyber-bullying others

A non-significant interaction was also found for cyber-bullying between country and bully/victim categorization (χ 2 [3] = 4.7, p = .192) Further analysis yielded significant effects for the bully/victim categorization (with all comparisons between categories significant) and country (see Table ​ Table4). 4 ). Those who bullied using traditional methods (bullies and bully-victims) reported higher levels of cyber-bullying than those victimised or not involved, with bully-victims reporting higher frequencies than bullies (see also Table ​ Table5). 5 ). Additionally, the Australian students tended to report more frequently engaging in cyber-bullying behaviours than the Swiss students.

(Cyber)bullying/victimisation and depressive symptoms (multivariable analyses)

To analyse differences between traditional bullies, victims, and bully-victims in relation to depressive symptoms, the same modelling procedure as described above was used. In the first analysis, only traditional bully/victim categorization was used (including a test of the interaction with country) with age and gender entered as control variables. In the second analysis, cyber-bullying and cyber-victimisation (as well as their interactions with country) were entered as additional independent variables.

Traditional bullying/victimisation

The analysis found that the effect of bully-victim categorization was not moderated by country (χ 2 [3] = 6.0, p = .113). The interaction term was dropped from the analyses. However, bully-victim categorization was a significant predictor of depressive symptoms. In addition, significant gender and country effects emerged (see Table ​ Table4). 4 ). Female students reported higher levels of depressive symptoms (z = 3.14, p = .002) whilst the Australian students had lower scores on average than the Swiss (z = -3.46, p = .001). When comparing the traditional bully-victim categories, all were significantly different from each other, with bully-victims having the highest levels of symptoms, followed by victims, then bullies; non-involved students had the least depressive symptoms (see also Table ​ Table5 5 ).

Cyber-bullying/victimisation as additional risk factor

First, the interactions between each of cyber-bullying and cyber-victimisation and country were tested to assess whether their association with depressive symptoms differed in Australia and Switzerland. As neither of these interaction effects reached significance (cyber-victimisation*country: z = .39, p = .697; cyber-bullying*country: z = 1.76, p = .078), they were dropped from the final model. Upon entering cyber-bullying and cyber-victimisation as additional independent variables, the main effects of traditional bully-victim behaviours remained the same (see Table ​ Table4), 4 ), except that the comparison between bullies and non-involved students and the comparison between victims and bully-victims were no longer significant. In addition, cyber-victimisation was a significant predictor of depressive symptoms, the more frequent the victimisation the higher the level of depressive symptoms (z = 4.83, p < .001).

This study examined the relationship between bullying and victimisation and symptoms of depression in adolescents from two different countries, Switzerland and Australia. Particular attention was paid to different forms of bullying behaviour - specifically traditional forms of bullying (including physical or verbal harassment) and cyber-bullying (using the Internet and/or mobile phone). While the association between traditional and cyber forms of bullying is established [ 49 ], to date it remains unclear if being cyber-victimised (over and above traditional victimisation) is associated with increased symptom endorsement.

Although in its relative infancy, the emergent research literature describing the outcomes associated with cyber-bullying/cyber-victimisation is largely consistent with the traditional bullying literature illustrating the robust negative relationship between all forms of bullying/victimisation and mental health. However, what has not yet been clearly described is the cumulative effect of being bullied via traditional and cyber means on the mental health of young people [ 6 ]. Thus, the third aim of this study was to investigate whether in adolescents, cyber-victimisation is an independent predictor of depressive symptoms, after accounting for self-reported traditional bullying victimisation and to determine the influence of study location (i.e., country) on this association.

Overlap between traditional and cyber-bullying/victimisation

The first hypothesis, which proposed a relationship between traditional and cyber forms of bullying and victimisation, was supported with statistically significant relationships between traditional and cyber forms of bullying perpetration and victimisation in the expected direction. Importantly, significant correlations were found between cyber-victimisation and gender (female), age, traditional bullying perpetration and victimisation. Furthermore, as participants aged, their self-reported bullying perpetration (traditional and cyber) increased, a relationship that remained significant only in the Australian sample when country-specific report was examined. Overall, all associations were stronger in the Australian sample.

These results add to the theoretical [ 5 ] and other empirical evidence [ 1 , 4 , 36 - 39 ] demonstrating the relationship between traditional and cyber forms of bullying perpetration and victimisation. In accordance with other studies, our findings suggest that traditional and cyber-bullying form part of the same cluster of socially inappropriate behaviours and argue for a behavioural versus technical approach to intervention programs.

Traditional victimisation and depressive symptoms

It was also hypothesized that those victimised using traditional methods (victims and bully-victims) would endorse more symptoms of depression than those who only reported bullying perpetration. Support for this hypothesis was found demonstrating that students who reported being victimised and bullying others as well as those only victimised were more likely to report depressive symptoms than were those who reported bullying perpetration only. This result was not moderated by country, indicating that the associations were comparable in both countries.

Cyber-victimisation and depressive symptoms

Finally, it was hypothesized that cyber-victimisation would represent an additional risk factor - independent of traditional victimisation - for the development of symptoms of depression. Strong support was found for the independent association that cyber-victimisation has with symptoms of depression over and above traditional bullying victimisation i.e. cyber-victimisation accounts for a significant amount of the variation in depressive symptoms even after controlling for possible effects of traditional victimisation. Importantly, this association was not moderated by country, which suggests that the relationship is not culturally dependent.

However, several differences between countries were found. For example, while Swiss students were more likely to report bullying others, the Australian students who bully others were more likely to report also using cyber-strategies. Despite these differences, it was demonstrated that cyber-victimisation was a significant predictor of depressive symptoms - a result that was culturally independent. This result suggests an additional negative mental health status associated with being exposed to bullying via technology, over and above that of being victimised by traditional means. Although fewer students reported being cyber-bullied via technology than traditional methods in both countries, clearly the inclusion of technology represents a risk factor for significantly higher rates of internalizing disorders for those victimised using both cyber and traditional methods.

Practical implications

The implications of these findings are important (e.g., for intervention programs) and demonstrate the scope of negative impact associated with cyber-victimisation. It is suggested that certain features of cyber-bullying (e.g., anonymity of perpetrator, accessibility of victim) present additional and difficult challenges for young people who are victimised [ 49 ]. It is often assumed that these challenges could contribute to a worsened mental health state for those victimised and the results of this study provide evidence in support of this.

Furthermore, some of the cyber-bullying strategies employed (e.g., nasty comments on SNS profiles) [ 4 ] mean that the audience potentially aware of the harassment is significantly larger. For example, if mean and nasty comments are posted on a SNS profile (social networking sites) or if an embarrassing picture is posted and the victim is identified in the picture by name (i.e., being tagged ), all people in their network, in addition to other networks, can potentially see that humiliating content. Therefore, strategies against cyber-bullying should also include educating students about privacy settings and safe internet/mobile practices. Given the difficulty in removing comments or pictures from the Internet and the permanence of information shared online, it is not surprising that cyber-victimisation represent an additional and independent risk factor for the development of depressive symptomatology. Further investigation is needed to clarify if specific elements of cyber-victimisation that are associated with poorer mental health outcomes for young people. For example, what is the impact of bullying via social networking sites given comments, pictures, and video can be viewed by a larger network (i.e., more students). Nonetheless, the results of this study raise important questions, as well as concerns, for those young people experiencing mental health issues in addition to bullying via traditional and cyber methods.

Strengths and Limitations

There were a number of strengths to this study. This was the first study to describe cultural similarities in relation to the impact of cyber-victimisation on depressive symptom endorsement. Despite some cultural differences (e.g., more Australian students reported using multiple strategies to bully (traditional and cyber) compared to Swiss students), the evidence demonstrating the additive effect of cyber-victimisation on mental health is an important result. Furthermore, the (culturally independent) predictive nature of cyber-victimisation on depressive symptoms provides an important insight into the influence of technology on young people.

Overall, there were some limitations with this study. For example, some items that assessed bullying and victimisation were worded differently between the two data collection countries. Moreover, there were certain differences in the wording of response categories and number of items in both samples. Regarding cyber-bullying/victimisation, we found a significant difference between Swiss and Australian students regarding their use of cyberstrategies to bully others (Australians reporting higher levels of cyber-bullying/victimisation). This finding has important methodological implications. Swiss students reported on two rather global items on cyber-bullying, whereas Australian students reported on five different behavioural descriptors of cyber-bullying. This might have lead to an underreporting of cyber-bullying in Swiss students. Studies in traditional bullying research have shown that global items result in lower prevalence rates of bullying than specific behavioural items [ 50 ].

Regarding depressive symptoms, it is important to know that although Australian students reported on more items than Swiss students, the same number of symptoms were assessed (i.e. the Australian students reported on two items for each symptom, Swiss students on 1-2 items). Nevertheless, we found a significant country effect on depressive symptoms. We assume that these country differences are mainly due to methodological differences. It is unlikely that the differences are culturally-based given the similarities between Switzerland and Australia in relation to the prevalence of depressive symptomatology [ 51 , 52 ].

There were some sample limitations (Swiss sample comprised students whose teachers volunteered while the Australian sample is comprised of students at religious-affiliated schools only), however, we do not anticipate that the associations examined would differ markedly from those in the general student population. Although there were some differences in sample demographics (e.g. age), these did not have an impact on the relationship between cyber-victimisation and self-reported depressive symptoms. Moreover, samples were highly similar regarding their access to technology. Other limitations concern the nature of the data collected. First, all measures were self-reports. Second, as with all cross-sectional studies the causal direction of the relationships cannot be determined, and thus our focus has been on associations between the variables involved.

In conclusion, this study provided evidence of a significant association between traditional and cyber forms of bullying behaviours. We demonstrated that, although several cultural differences exist between Swiss and Australian participants in relation to bullying and victimisation, the relationship between cyber-victimisation and increased endorsement of depression symptoms was culturally independent.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

SP and JD were responsible for the conceptual background of the paper, analyzed and interpreted the data and drafted the manuscript. TS analysed and interpreted the data. DC is grant-holder, conceived and directed the Australian study, and was actively involved in writing up the manuscript. All authors read and approved the final manuscript.

• Smith PK, Mahdavi J, Carvalho M, Fisher S, Russell S, Tippett N. Cyberbullying: Its nature and impact in secondary school pupils. Journal of Child Psychology and Psychiatry. 2008; 49 :376–385. doi: 10.1111/j.1469-7610.2007.01846.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Li Q. Cyberbullying in Schools: A Research of Gender Differences. School Psychology International. 2006; 27 :157–170. doi: 10.1177/0143034306064547. [ CrossRef ] [ Google Scholar ]
• Cassidy W, Jackson M, Brown KN. Sticks and Stones Can Break My Bones, But How Can Pixels Hurt Me?: Students' Experiences with Cyber-Bullying. School Psychology International. 2009; 30 :383. doi: 10.1177/0143034309106948. [ CrossRef ] [ Google Scholar ]
• Cross D, Shaw T, Hearn L, Epstein M, Monks H, Lester L, Thomas L. Australian Covert Bullying Prevalence Study (ACBPS) Perth, Child Health Promotion Research Centre, Edith Cowan University; 2009. [ Google Scholar ]
• Dooley J, Pyzalski J, Cross D. Cyberbullying Versus Face-to-Face Bullying. A Theoretical and Conceptual Review. Zeitschrift für Psychologie/Journal of Psychology. 2009; 217 :182–188. doi: 10.1027/0044-3409.217.4.182. [ CrossRef ] [ Google Scholar ]
• Gradinger P, Strohmeier D, Spiel C. Traditional bullying and cyberbullying: Identification of risk groups for adjustment problems. Zeitschrift fur Psychologie/Journal of Psychology. 2009; 217 :205–213. [ Google Scholar ]
• Ladd GW. Children's peer relations and social competence. A century of progress. New Haven, Yale University Press; 2005. [ Google Scholar ]
• Kochenderfer BJ, Ladd GW. Peer victimization: Cause or consequence of school maladjustment? Child Development. 1996; 67 :1305–1317. doi: 10.2307/1131701. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Perren S, Von Wyl A, Stadelmann S, Burgin D, von Klitzing K. Associations between behavioral/emotional difficulties in kindergarten children and the quality of their peer relationships. Journal of the American Academy of Child and Adolescent Psychiatry. 2006; 45 :867–876. doi: 10.1097/01.chi.0000220853.71521.cb. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gazelle H, Ladd GW. Anxious solitude and peer exclusion: A diathesis-stress model of internalizing trajectories in childhood. Child Development. 2003; 74 :257–278. doi: 10.1111/1467-8624.00534. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goodman MR, Stormshak EA, Dishion TJ. The significance of peer victimization at two points in development. Journal of Applied Developmental Psychology. 2001; 22 :507–526. doi: 10.1016/S0193-3973(01)00091-0. [ CrossRef ] [ Google Scholar ]
• Hanish LD, Guerra NG. A longitudinal analysis of patterns of adjustment following peer victimization. Development and Psychopathology. 2002; 14 :69–89. doi: 10.1017/S0954579402001049. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hodges EVE, Perry DG. Personal and interpersonal antecedents and consequences of victimization by peers. Journal of Personality and Social Psychology. 1999; 76 :677–685. doi: 10.1037/0022-3514.76.4.677. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Arseneault L, Milne BJ, Taylor A, Adams F, Delgado K, Caspi A, Moffit TE. Being bullied as an environmentally mediated contributing factor to children's internalizing problems. Archives of Paediatric and Adolecent Medicine. 2008; 162 :145–150. doi: 10.1001/archpediatrics.2007.53. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hawker DSJ, Boulton M. Twenty years' research on peer victimization and psychosocial maladjustment: A meta-analytic review of cross-sectional studies. Journal of Child Psychology and Psychiatry and Allied Disciplines. 2000; 41 :441–455. doi: 10.1111/1469-7610.00629. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Alsaker FD, Olweus D. Stability and change in global self-esteem and self-related affect. SUNY series, studying the self 2002, Understanding early adolescent self and identity: Applications and interventions. pp. 193–223.
• Graham S, Bellmore AD, Mize J. Peer Victimization, Aggression, and Their Co-Occurrence in Middle School: Pathways to Adjustment Problems. Journal of Abnormal Child Psychology. 2006; 34 :363–378. doi: 10.1007/s10802-006-9030-2. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nishina A, Juvonen J, Witkow MR. Sticks and Stones May Break My Bones, but Names Will Make Me Feel Sick: The Psychosocial, Somatic, and Scholastic Consequences of Peer Harassment. Journal of Clinical Child and Adolescent Psychology. 2005; 34 :37–48. doi: 10.1207/s15374424jccp3401_4. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rigby K. Health consequences of bullying and its prevention in schools. Peer harassment in school: The plight of the vulnerable and victimized 2001, Peer harassment in school: The plight of the vulnerable and victimized. pp. 310–331.
• Storch EA, Phil M, Nock MK, Masia Warner C, Barlas ME. Peer Victimization and Social-Psychological Adjustment in Hispanic and African-American Children. Journal of Child and Family Studies. 2003; 12 :439–452. doi: 10.1023/A:1026016124091. [ CrossRef ] [ Google Scholar ]
• Stassen Berger K. Update on bullying at school: Science forgotten? Developmental Review. 2007; 27 :90–126. doi: 10.1016/j.dr.2006.08.002. [ CrossRef ] [ Google Scholar ]
• Cillessen AHN, Mayeux L. From censure to reinforcement: Developmental changes in the association between aggression and social status. Child Development. 2004; 75 :147–163. doi: 10.1111/j.1467-8624.2004.00660.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Perren S, Alsaker FD. Social behavior and peer relationships of victims, bully-victims, and bullies in kindergarten. Journal of Child Psychology and Psychiatry. 2006; 47 :45–57. doi: 10.1111/j.1469-7610.2005.01445.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Laursen B, Finkelstein BD, Townsend-Betts N. A developmental meta-analysis of peer conflict resolution. Developmental Review. 2001; 21 :423–449. doi: 10.1006/drev.2000.0531. [ CrossRef ] [ Google Scholar ]
• Haynie DL, Nansel T, Eitel P, Crump AD, Saylor K, Yu K, Simons-Morton B. Bullies, victims, and bully/victims: Distinct groups of at-risk youth. Journal of Early Adolescence. 2001; 21 :29–49. doi: 10.1177/0272431601021001002. [ CrossRef ] [ Google Scholar ]
• Picket W, Schmid H, Boyce W, Simpson K, Scheidt PC, Mazur J. Multiple risk behavior and injury: an international analysis of young people. Archives of Pediatrics and Adolescent Medicine. 2002; 156 :786–793. [ PubMed ] [ Google Scholar ]
• Nansel TR, Craig W, Overpeck MD, Saluja G, Ruan WJ. Cross-national consistency in the relationship between bullying behaviors and psychosocial adjustment. Archives of Pediatrics and Adolescent Medicine. 2004; 158 :730–736. doi: 10.1001/archpedi.158.8.730. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Perren S, Hornung R. Bullying and Delinquency in Adolescence: Victims' and Perpetrators' Family and Peer Relations. Swiss Journal of Psychology. 2005; 64 :51–64. doi: 10.1024/1421-0185.64.1.51. [ CrossRef ] [ Google Scholar ]
• Moffitt TE. Adolescence-limited and life-course-persistent antisocial behavior: A developmental taxonomy. Psychological Review. 1993; 100 :674–701. doi: 10.1037/0033-295X.100.4.674. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kumpulainen K, Rasanen E, Henttonen I. Children involved in bullying: Psychological disturbance and the persistence of the involvement. Child Abuse and Neglect. 1999; 23 :1253–1262. doi: 10.1016/S0145-2134(99)00098-8. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kaltiala-Heino R, Rimpela M, Marttunen M, Rimpela A, Rantanen P. Bullying, depression, and suicidal ideation in Finnish adolescents: school survey. British Medical Journal. 1999; 319 :348–351. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Menesini E, Modena M, Tani F. Bullying and Victimization in Adolescence: Concurrent and Stable Roles and Psychological Health Symptoms. Journal of Genetic Psychology. 2009; 170 :115–133. doi: 10.3200/GNTP.170.2.115-134. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mason KL. Cyberbullying: A preliminary assessment for school personnel. Psychology in the Schools. 2008; 45 :323–348. doi: 10.1002/pits.20301. [ CrossRef ] [ Google Scholar ]
• Mitchell KJ, Ybarra M, Finkelhor D. The relative importance of online victimization in understanding depression, delinquency, and substance use. Child Maltreatment. 2007; 12 :314. doi: 10.1177/1077559507305996. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hinduja S, Patchin JW. Cyberbullying: An exploratory analysis of factors related to offending and victimization. Deviant Behavior. 2008; 29 :129–156. doi: 10.1080/01639620701457816. [ CrossRef ] [ Google Scholar ]
• Kowalski RM, Limber SP. Electronic bullying among middle school students. Journal of Adolescent Health. 2007; 41 :22–30. doi: 10.1016/j.jadohealth.2007.08.017. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Raskauskas J, Stoltz AD. Involvement in traditional and electronic bullying among adolescents. Developmental Psychology. 2007; 43 :564–575. doi: 10.1037/0012-1649.43.3.564. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Slonje R, Smith PK. Cyberbullying: Another main type of bullying? Scandinavian Journal of Psychology. 2008; 49 :147–154. doi: 10.1111/j.1467-9450.2007.00611.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ybarra ML, Mitchell KJ. Youth engaging in online harassment: associations with caregiver-child relationships, Internet use, and personal characteristics. Journal of Adolescence. 2004; 27 :319–336. doi: 10.1016/j.adolescence.2004.03.007. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Juvonen J, Gross EF. Extending the school grounds? Bullying experiences in cyberspace. The Journal of School Health. 2008; 78 :496–505. doi: 10.1111/j.1746-1561.2008.00335.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ortega R, Elipe P, Mora-Merchan JA, Calmaestra J, Vega E. The emotional impact on victims of traditional bullying and cyberbullying: A study of Spanish adolescents. Zeitschrift fur Psychologie/Journal of Psychology. 2009; 217 :197–204. [ Google Scholar ]
• Spears B, Slee P, Owens L, Johnson B. Behind the scenes and screens: Insights into the human dimension of covert and cyberbullying. Zeitschrift fur Psychologie/Journal of Psychology. 2009; 217 :189–196. [ Google Scholar ]
• Eslea M, Menesini E, Morita Y, O'Moore M, Mora-Merchán JA, Pereira B, Smith PK. Friendship and loneliness among bullies and victims: Data from seven countries. Aggressive Behavior. 2004; 30 :71–83. doi: 10.1002/ab.20006. [ CrossRef ] [ Google Scholar ]
• Bernet M, Schläpfer J. "Cyberbullying" als Mobbingform unter Jugendlichen in der Schweiz: Phänomen, Risiken und Konsequenzen. Jacobs Center for Productive Youth Development. 2009. Lizentiatsarbeit.
• Lovibond SH, Lovibond PF. Manual for the Depression Anxiety Stress Scales. Sydney, Psychology Foundation; 1995. [ PubMed ] [ Google Scholar ]
• Alsaker FD. Pubertal timing, overweight, and psychological adjustment. Journal of Early Adolescence. 1992; 12 :396–419. doi: 10.1177/0272431692012004004. [ CrossRef ] [ Google Scholar ]
• Holsen I, Kraft P, Vitterso J. Stability in Depressed Mood in Adolescence: Results from a 6-Year Longitudinal Panel Study. Journal of Youth and Adolescence. 2000; 29 :61–78. doi: 10.1023/A:1005121121721. [ CrossRef ] [ Google Scholar ]
• Osgood DW, Finken LL, McMorris BJ. Analyzing multiple-item measures of crime and deviance II: Tobit regression analysis of transformed scores. Journal of Quantitative Criminology. 2002; 18 :319–347. doi: 10.1023/A:1021198509929. [ CrossRef ] [ Google Scholar ]
• Dooley J, Cross D, Hearn L, Treyvaud R. Review of existing Australian and international cyber-safety research. Perth, Edith Cowan University, Child Health Promotion Research Center; 2009. [ Google Scholar ]
• Vaillancourt T, Trinh V, McDougall P, Duku E, Cunningham L, Cunningham C, Hymel S, Short K. Optimizing Population Screening of Bullying in School-Aged Children. Journal of School Violence. 2010; 9 :233–250. doi: 10.1080/15388220.2010.483182. [ CrossRef ] [ Google Scholar ]
• Boyd CP, Kostanski M, Gullone E, Ollendick TH, Shek DT. Prevalence of anxiety and depression in Australian adolescents: comparisons with worldwide data. Journal of Genetic Psychology. 2000; 161 :479–492. doi: 10.1080/00221320009596726. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Steinhausen HC, Metzke CW. Adolescent self-rated depressive symptoms in a Swiss epidemiological study. Journal of Youth and Adolescence. 2000; 29 :427–440. doi: 10.1023/A:1005106409022. [ CrossRef ] [ Google Scholar ]