Online Social Networking Behaviours, Cyber-Bullying

Online Social Networking Behaviours, Cyber-Bullying and Mental Health in Australian Students

Julian J. Dooley Adrian J. Scott

Acknowledgements The authors would like to thank Eleanor Rudnai, Steffanie Ransom and Luke FundellWilliamson for their assistance with the data collection.

Abstract The present study comprised 599 West Australian students (48.4% male), aged between 12 and 16 years (M = 13.98 years, SD = 0.88) who participated in a survey relating to technology use, cyber-safety, bullying and cyber-bullying, mental health and behavioural functioning. Students’ SNS use was widespread with 61.9% of students using them 6 to 7 days a week. Most of this use occurred on the weekend with 80.2% of students using SNS for one or more hours on an average day on the weekend compared to 58.5% on an average day before or after school and 12.8% on an average day at school. A large proportion (69.8%) of students had 200 or more online friends (49.1% had 300 or more) and 92.0% reported knowing all or nearly all of them in person (35.7% knowing all). Students who were online (specifically on their SNS) for more time (weekday and weekend) and were younger were more likely to be cyber-bullied. With regard to mental health and behavioural functioning, female students experienced higher levels of depression and anxiety, and more behavioural problems than male students. Students who reported using more SNS and being online for longer over the weekend also experienced higher levels of depression and anxiety, and more behavioural problems. Finally, students who reported being online for longer at school and knowing fewer of their SNS friends in person experienced more behavioural problems. The results of this study offer important insights into SNS use and behaviours, as well as their impact on cyber-victimisation. The results have significant implications for the way in which young people interact with peers via SNS, for the cyber-bullying behaviours students might be exposed to, and for the provision of appropriate support and intervention strategies necessary to minimise mental health and behavioural problems.

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Introduction Communication with others is one of the primary reasons why people use the Internet (Pollet, Roberts & Dunbar, 2011). After all, the Internet is essentially a communication tool – designed to share information with a global audience – that continues to develop and change with the introduction of new technologies (e.g., smartphones). The most notable development in relation to Internet usage over the past decade has been the proliferation of Social Networking Sites (SNS). These sites are built around social connection and are consistently among the most commonly visited sites online (see www.alexa.com for latest information on global and national website usage). SNS provide an easy opportunity to communicate with others, connect with groups, organise activities and, ultimately, have some (positive or negative) impact on society (Rainie, Purcell & Smith, 2011). In the past five years, much has been written describing the demographics of SNS users. Recently, Hampton and colleagues (Hampton, Goulet, Rainie & Purcell, 2011) reported that SNS users in the USA are typically under 35 years of age (although this overrepresentation of younger users is changing as SNS sites become more widely popular), female, have multiple SNS accounts, have been using the sites for at least a year and use their site several times a day. Similarly, as many as 95% of Australian students in Years 7 to 10 have a SNS profile (de Zwart, Lindsay, Henderson & Phillips, 2011). Currently, the most popular site is Facebook (with nearly one billion active users worldwide) with MySpace, Twitter and LinkedIn also being widely used. These sites may have the largest number of users but they represent a small percentage of the total number of SNS online. There are, for example, SNS aimed at young children, academics, avid travellers, social activists, people with disabilities, artists and musicians to name just a few. Given the range of SNS available, there are many benefits and opportunities associated with these sites. For example, frequenting these sites can reduce social isolation

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and increase social support, can promote self-expression and identity development, can promote social engagement and activism, can revive dormant or old relationships and can promote education and knowledge sharing (Hampton et al., 2011; Livingstone & Brake, 2010). Thus, SNS provide opportunities to develop and enhance social relationships as well as build new ones (Lenhart, Madden, Smith, Purcell, Zickuhr & Rainie, 2011). Although there are numerous benefits to SNS use, there are clearly risks associated with being active on these sites. For example, sharing information on SNS has been associated with an increased risk of online victimisation (e.g., Walrave & Heirman, 2009). This online victimisation is often referred to as cyber-bullying which typically encompasses a complex set of repeated aggressive behaviours intentionally designed to inflict harm on another person through the use of technology (Dooley, Pyzalski & Cross, 2009; Hanewald, 2009). Cyber-bullying is often seen as an extension of offline (or traditional) bullying which has some empirical support (e.g., Gradinger, Strohmeier & Spiel, 2009). However, this approach overlooks important conceptual and functional differences between cyber- and traditional bullying. For example, while traditional bullying is built on the premise that the behaviour must be repeated (otherwise it is considered aggression, not bullying; see Olweus, 1993), cyberbullying can easily result from a single action (e.g., posting an embarrassing video online) which can be repeatedly experienced by the person victimised. Furthermore, recent evidence has raised some doubts about the intentionality of cyber-bullying behaviours. For example, Shapka (2011) reported that the majority of aggressive online messaging by adolescents was ‘just kidding around or having fun with friends’, with only a small minority perceived as truly intending to ‘hurt or embarrass another person’. Additionally, the nature of online exchange can impact on both the delivery and experience of cyber-bullying behaviours. Law and colleagues (Law, Shapka, Domene, Gagné, 2012b) highlighted important differences

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between immediate and delayed reinforcement where the awareness of some behaviours (e.g., sending a mean message) may be proximal to the perpetration while others (e.g., building a website) may be more distal. Interestingly, Law and colleagues (Law, Shapka, Hymel, Olson & Waterhouse, 2012a) described a similar phenomenon in relation to the perpetration of cyber-bullying whereby adolescents identified themselves with the method of aggression they used (e.g., sending mean messages or creating hostile websites) rather than the role they played (i.e., bully, victim or witness). These conceptual differences and theoretical challenges have likely contributed to the notable issues related to the measurement of cyber-bullying behaviours. For example, reported prevalence estimates have ranged from 9% to 40% (David-Ferdon & Hertz, 2009; Tokunaga, 2010; Ybarra, Mitchell, Wolak & Finkelhor, 2006). Nevertheless, it is important to note that cyber-bullying rates are almost always lower than traditional bullying rates, with recent USA estimates placing the prevalence of cyber-bullying at 16% compared to 26% for traditional bullying (Kessel Schneider, O’Donnell, Stueve & Coulter, 2012). Regardless of academic discussions about the conceptual nature of cyber-bullying, there is strong evidence that these experiences can result in significant mental health problems. For example, numerous cross-sectional studies have demonstrated that cyberbullying is associated with depression (Perren, Dooley, Shaw & Cross, 2010), conduct and emotional problems (Dooley, Gradinger, Strohmeier, Cross & Spiel, 2010; Gradinger, Strohmeier & Spiel, 2009), not feeling safe at school, physical pain, substance misuse, and abuse (Sourander et al., 2010). Important correlates of the mental health outcomes after cyber-bullying relate to the coping strategies and support networks of the person victimised. Consistently, young people who are victimised are more likely to exhibit psychological ill health if they have ineffective coping strategies or feel unsupported during the experience (Cassidy & Taylor, 2005). An

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important element of coping is problem-solving style, a pattern of cognitive functioning, which has a significant influence on how people respond to social problems and deal with life stress. For example, ineffective problem-solving styles have been linked with aggressive (e.g., Crick & Dodge, 1999) and bullying behaviours (e.g., Warden & MacKinnon, 2003). Crick and Dodge (1994) also highlighted the importance of peer evaluation and feedback in response to social behaviours and suggested that the more peers support a behaviour, the more likely it is to occur in the future. In the same vein, the more support provided to victims of bullying, the more positive the mental health outcome. Thus, SNS can offer a potential benefit given the nature and extent of social connection, and result in an immediate source of support if cyber-bullying occurs openly (as is sometimes the case). For example, Dooley and Scott (under review) demonstrated that mental health problems were more likely to be experienced by young people who were privately victimised (e.g., through text messages or emails) compared to those who were publicly victimised (e.g., through nasty messages or pictures posted on SNS). Although speculative, the authors suggested that the openness of the victimisation might prompt other users to provide support, thus ameliorating mental health problems. Importantly, it is unlikely that support is merely a numbers game where more online friends equals more support. It is more likely that the number of close friends (i.e., those known offline) results in a greater level of support and, as a result, reduced mental health problems. Ultimately, this issue highlights the complexities associated with SNS use, and the many potential benefits and risks associated with their use. In this chapter, we examine the SNS use of Australian students to determine if there are patterns that predict cybervictimisation, mental health and behavioural functioning.

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Method Participants A total of 827 students participated in a survey concerning Internet and mobile phone use. All data were collected between November 2011 and March 2012. Students from seven government high schools (out of 11 that were originally contacted) in the metropolitan area of Perth, Western Australia, were recruited to participate in the study. Of the 827 students, 162 (19.6%) were excluded from the current study because they were not active users of SNS, and a further 66 (8.0%) were excluded because they had not completed all relevant sections of the survey. The final sample comprised 599 students, 290 (48.4%) male, aged between 12 and 16 years (M = 13.98, SD = 0.88). Students were from Years 8 to 10; with 285 (47.6%) from Year 8, 114 (19.0%) from Year 9, and 198 (33.1%) from Year 10. Two students (0.3%) did not provide their school year. There was little difference in the number of SNS used by students across the three year groups with averages of 2.41 (SD = 1.04) for Year 8, 2.65 (SD = 1.34) for Year 9 and 2.43 (SD = 1.11) for Year 10. Materials: The survey contained items relating to technology use, cyber-safety practices/strategies, bullying and cyber-bullying, mental health, behavioural functioning, as well as demographic information. Cyber-bullying. The 11-item Cyber-Victimisation Scale measured students’ selfreported experiences of being cyber-bullied. Students indicated on a 5-point Likert scale how often they experienced each of 11 behaviours, which included being sent nasty e-mails or nasty messages on the Internet, having mean or nasty comments posted on websites (e.g., MySpace or Facebook), or being left out or ignored over the Internet. Their responses ranged from 0 ‘Never’ to 4 ‘Most days this term’.

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Mental health. Mental health was assessed using a modified version of the Depression, Anxiety and Stress Scales (DASS; Lovibond & Lovibond, 1995) where only the depression and anxiety scales were used. Responses range from 0 ‘Does not apply to me’ to 3 ‘Most of the time’ and each scale consisted of seven items. Depression (Cronbach’s alpha = .91) and anxiety items (Cronbach’s alpha = .0.82) were totalled to produce separate depression and anxiety scores ranging from 0 to 21. Behavioural functioning. Behaviour problems were assessed using the Strengths and Difficulties Questionnaire (SDQ; Goodman, 2001). The SDQ provides a total difficulties score on the basis of four subscale scores – emotional symptoms, conduct problems, hyperactivity and peer problems – with responses ranging from 0 ‘Not true’ to 2 ‘Certainly true’. The scores of the 20 items (Cronbach’s alpha = 0.75) were totalled to produce a total difficulties score ranging from 0 to 40. Procedure The Human Research Ethics Committee of Edith Cowan University and the West Australian Department of Education approved this study. Once schools agreed to participate, information statements and consent forms were delivered two weeks prior to data collection. On the days of data collection, researchers visited the school and, through collaboration with teachers, only students who had agreed to participate in the survey and had signed parental consent forms were provided with the web link for the survey. In almost all cases (96.0%), surveys were completed online. However, paper-and-pencil versions were used in a small number of classes where students either did not have their own laptop or it was not possible to book the school computer laboratory. All students were debriefed about the aims of the study upon completion of the survey.

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Results Regression analyses were performed to assess the impact of nine independent variables (age; number of SNS used; sex; number of days used SNS a week; average number of hours used SNS at school, before and after school and on the weekend; number of SNS friends/contacts; and proportion of SNS friends/contacts known in person) on four dependent variables (exposure to cyber-victimisation; depression and anxiety; and total difficulties). Descriptive statistics for the independent and dependent variables are provided in Tables I and II. ---Tables I and II about here--Table I shows that 61.9% of students used SNS 6 to 7 days a week and that most of this use occurred on the weekend: 80.2% of students used SNS for one or more hours on an average day on the weekend compared to 58.5% on an average day before or after school and only 12.8% on an average day at school. It is also apparent that 69.8% of students had 200 or more SNS friends/contacts, and that 92.0% knew all or nearly all of them in person. With regard to the mental health and behavioural functioning of students, Table II shows that student depression, anxiety and total difficulties scores were low, with the average scores of 3.50, 2.72 and 11.28 falling well below the maximum possible scores of 21, 21 and 40 respectively. Finally, just over a quarter (28.9%) of students had been exposed to cybervictimisation. Cyber-bullying: Exposure to cyber-victimisation. Logistic regression (backward method) analysis was performed to assess the impact of the nine independent variables on exposure to cyber-victimisation. The final model contained four independent variables: average number of hours used SNS on the weekend, age, average number of hours used SNS at school and average number of SNS used. The model was statistically significant, χ2(4, 599) = 49.14, p < .001, indicating that it was able to distinguish between students who had

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been exposed to cyber-victimisation and those who had not. The model explained between 7.9% (Cox and Snell R square) and 11.3% (Nagelkerek R square) of the variance in exposure to cyber-victimisation, and correctly classified 71.6% of cases. The B coefficients, standard errors, odds ratios and Wald statistics for the final model of the logistic regression analysis are provided in Table III. ---Table III about here--The analysis revealed that students who were exposed to cyber-victimisation used SNS for a greater number of hours on an average day on the weekend and on an average day at school, than students who were not exposed to cyber-victimisation (p < .001 and p < .05 respectively). Furthermore, students who were exposed to cyber-victimisation used more SNS and were younger than students who were not exposed to cyber-victimisation (p < .05 and p < .01 respectively). Mental health: Depression and anxiety. Regression (backward method) analyses were performed to assess the relationship between the nine independent variables and the depression and anxiety scores. The final model for depression contained three independent variables: sex, average number of hours used SNS on the weekend and number of SNS used. The model was statistically significant, F(3, 595) = 20.37, p < .001, and explained 8.8% of variance in depression scores. The final model for anxiety contained the same three independent variables: sex, number of SNS used and average number of hours used SNS on the weekend. The model was also statistically significant, F(3, 595) = 12.63, p < .001, and explained 5.5% of variance in anxiety scores. The B values, standard errors and standardised Beta values (β) for the final models of the regression analyses are provided in Table IV. ---Table IV about here--The analyses revealed that female students experienced more depression and anxiety than male students (p < .001 and p < .01 respectively). Furthermore, the more hours students

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used SNS on an average day on the weekend, the higher their depression and anxiety scores (p < .001 and p < .05 respectively); and the more SNS students used, the higher their depression and anxiety scores (both p < .01). Behavioural functioning: Total difficulties. Finally, regression (backward method) analysis was performed to assess the relationship between the nine independent variables and the total difficulties score. The final model contained five independent variables: number of SNS used, average number of hours used SNS on the weekend, sex, average number of hours used SNS at school and proportion of SNS friends/contacts known in person. The model was statistically significant, F(5, 593) = 17.36, p < .001, and explained 12.0% of variance. The B values, standard errors and standardised Beta values (β) for the final model of the regression analysis are provided in Table V. ---Table V about here--Similar to depression and anxiety, female students experienced more difficulties than male students (p < .01); the more hours students used SNS on an average day on the weekend, the higher their total difficulties scores (p < .01); and the more SNS students used, the higher their total difficulties scores (p < .001). In addition, the more hours students used SNS on an average day at school, and the less SNS friends/contacts students knew in person, the higher their total difficulties scores (both p < .05). Conclusion SNS are among the most popular online activities, especially for young people. These sites offer many important benefits, such as connecting with friends, developing a social identity, becoming socially conscious and increasing social support (Hampton et al., 2011; Livingstone & Brake, 2010). Although there is tremendous potential to facilitate the provision of support through these sites, there also exists the potential to experience cyberbullying. However, little is known about the patterns of SNS use and the associated potential

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for cyber-victimisation. As such, this chapter focused on the experience of cybervictimisation, mental health and behavioural functioning in SNS users. In relation to the experience of cyber-victimisation, students who were online (specifically on their SNS) for more time (weekday and weekend) were more likely to be cyber-bullied. In addition, younger students were more likely to be cyber-bullied than older students. These are critically important results associated with basic use factors. Importantly, even though older students had more friends than younger students, the latter were still more at risk of being cyber-bullied. Although it is not possible to know the identity of the perpetrator and their relationship to the victim, this result does raise important questions about the involvement of young people in SNS. Most of the commonly used SNS (e.g., Facebook, Twitter and MySpace) have a minimum age restriction of about 13 years of age. This designation is generally a legal one (websites that collect personal information are not permitted to do so from children under the age of 13 years) although some sites (e.g., Facebook) advocate that this minimum age restriction should be removed. In light of the results of this study, it appears that a reasonable approach to reducing cyber-victimisation would be to increase the minimum age of users. As noted, the primary SNS used by students in this study were Facebook and Twitter. In addition to the experience of cyber-bullying, students also reported experiencing mental health symptoms and behavioural problems. Specifically, female students reported more symptoms of depression and anxiety than did male students as did students who reported being online for longer over the weekend. Furthermore, the more SNS that students reported using, the higher their self-reported depression and anxiety scores. Consistent with mental health, more behaviour problems were experienced by female students, students who used more SNS and students who used their SNS more on an average day on the weekend.

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Importantly, the fewer SNS friends students knew in person, the higher their total difficulties scores. Thus, independent of being cyber-bullied, specific SNS use patterns predicted mental and behavioural health problems. This is an important result as the mental health problems that are reported by those students who frequently use SNS are likely to be exacerbated in the event that they are cyber-bullied. Perren and colleagues (Perren et al., 2010) reported that cyber-bullying resulted in an independent and additive effect on depression symptoms over and above the experience of traditional bullying. While this could be the result of being cyber-bullied, the results of this study indicate that, at least in part, the impact of SNS use (outside of cyber-bullying) should be considered. The results of this study also provide important insights to the implications of SNS use and the potential impact of being cyber-bullied. The more involved students were in their SNS, the more likely they were to report mental and behavioural health problems. The recent proliferation of smartphones (i.e., mobile phones that can connect to the Internet) make it easier to engage with SNS and other sites as they can be accessed at any time in any place provided there is an Internet connection available. This ease of connection makes it even more important that students are educated on ways to keep themselves safe as it is not feasible to monitor all of their activities. Thus, early education and the implementation of preventative measures become key strategies to ensure that the benefits of SNS are enhanced while the associated risks are minimised. Several important results were found in relation to the experience of being cyberbullied. For example, younger students who used SNS were more likely to be victimised. Dooley and colleagues have previously demonstrated that students with poor coping and social problem solving skills were more likely to avoid proactive help-seeking strategies and to respond aggressively (Dooley et al., 2011, Dooley et al., 2012). Given that social problem

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skills are considered to develop with age, it is not unreasonable that younger students might respond in socially inappropriate ways (e.g., through bullying or aggression). Given the potential for concurrent mental and behavioural health problems, there is a pressing need for developmentally appropriate preventative approaches to the safe use of SNS. Furthermore, the more students reported using SNS and the more sites they used, the more likely they were to report being cyber-bullied. Although it is not possible to have a ‘safe number’ in relation to the number of friends or the amount of time spent online, it is clear that some level of restriction is warranted. The more time spent on a SNS on the weekend and during the week predicted the likelihood of being cyber-bullied. Thus, one protective measure is to limit the amount of time spent online. Given the relationship between extent of use, mental health and behavioural functioning (independent of cyberbullying), restricting the amount of time spent on SNS is likely to have several benefits. Finally, the relationship between online friends and behavioural health is an important one. It was demonstrated that students who reported having less online friends that were known in person (i.e., offline), had less behaviour problems. Therefore, rather than the number of friends per se being important, it is the number of friends that are known in person that counts. There are several strengths and limitations of this study. The cross-sectional nature of the study does not make it possible to understand the temporal sequence of SNS use and mental and behavioural health problems. It may be that young people with mental health problems are drawn to using SNS or that SNS use results in symptoms of depression and anxiety. Regardless, the relatively high prevalence and strong predictive associations between SNS use and mental health problems should serve as a strong indicator of the need for preventative mental health messages and support services. It is not appropriate to wait until cyber-bullying is experienced, and mental health symptoms and behavioural problems

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occur – these services should be provided from the outset. Furthermore, the apparent sex differences reported in this study – whereby females reported more mental and behavioural health problems than males – provide a strong argument for sex specific information and support services. However, it is important to note that this could be a legitimate sex-based result or it could be based on an increased likelihood of females to self-report. The results of this study offer important insights into SNS use and behaviours, as well as their impact on cyber-victimisation. Furthermore, the results have significant implications for the way in which young people interact with peers via SNS, for the cyber-bullying behaviours students’ experience, and for the provision of appropriate school-based support and intervention strategies necessary to minimise mental health and behaviour problems. Of critical importance, the results of this study highlight the need for early preventative approaches to ensure that mental and behavioural health problems are minimised, restrictions are based on the amount of time and level of engagement with SNS, friends are comprised of those known offline and the number of SNS sites used is limited. These key factors represent the greatest opportunity for enhancing the likelihood of a positive experience and reducing the impact of SNS on mental health and behavioural functioning.

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References Cassidy, T., & Taylor, L. (2005). Coping and psychological distress as a function of the bully victim dichotomy in older children. Social Psychology of Education, 8, 249–262. Crick, N. R., & Dodge, K. A. (1994). A review and reformulation of social informationprocessing mechanisms in children’s social adjustment. Psychological Bulletin, 115, 74–101. Crick, N. R., & Dodge, K. (1999). ‘‘Superiority’’ is in the eye of the beholder: A comment on Sutton, Smith and Swettenham. Social Development, 8, 128–131. David-Ferdon C., & Hertz M. F. (2009). Electronic media and youth violence: A CDC issue brief for researchers. Atlanta, GA: Centers for Disease Control and Prevention. De Zwart, M., Lindsay, D., Henderson, M., & Phillips, M. (2011). Teenagers, legal risks and social networking sites. Monash University. Dooley, J. J., Gradinger, P., Stohmeier, D., Cross, D., & Spiel, C. (2010). Cybervictimisation: The association between help seeking and mental health symptoms in adolescents from Australia and Austria. Australian Journal of Guidance and Counselling, 20, 194-210. Dooley, J. J., Pyzalski, J., & Cross, D. (2009). Cyberbullying and face-to-face bullying: Similarities and differences. Zeitschrift für Psychologie/Journal of Psychology, 217, 182-188. Gradinger, P., Strohmeier, D., & Spiel, C. (2009). Traditional bullying and cyberbullying: Identification of risk groups for adjustment problems. Zeitschrift für Psychologie/Journal of Psychology, 217, 205-213. Hampton, K. N., Goulet, L. S., Rainie, L. & Purcell, K. (2011). Social networking sites and our lives: How people’s trust, personal relationships, and civic and political

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involvement are connected to their use of social networking sites and other technologies. Pew Internet & American Life Project. Hanewald, R. (2009). Cyberbullying research: the current state. Australian Educational Computing, 24, 10-15. Kessel Schneider S., O’Donnell L., Stueve A., Coulter R. W. (2012). Cyberbullying, school bullying, and psychological distress: A regional census of high school students. American Journal of Public Health, 102, 171-77 Law, D. M., Shapka, J. D., Hymel, S., Olson, B. F., & Waterhouse, T. (2012a). The changing face of bullying: An empirical comparison between traditional and internet bullying and victimization. Computers in Human Behavior, 28, 226-232. Law, D. M., Shapka, J. D., Domene, J. F., & Gagné, M. H. (2012b). Are cyberbullies really bullies? An investigation of reactive and proactive online aggression. Computers in Human Behavior, 28, 664-672. Lenhart, A., Madden, M., Purcell, A., Zickuhr, K., & Rainie, L. (2011). Teens, kindness and cruelty on social network sites. Pew Internet & American Life Project. Livingstone, S. & Brake, D. R. (2010). On the rapid rise of social networking sites: New findings and policy implications. Children & Society, 24, 75-83. Olweus, D. (1993). Bullying at school: What we know and what we can do. Cambridge, MA: Blackwell. Perren, S., Dooley, J. J., Shaw, T., & Cross, D. (2010). Being victimized in school and cyberspace: Associations with depressive symptoms in Swiss and Australian adolescents. Child and Adolescent Psychiatry and Mental Health, 4, 28. Pollet, T. V., Roberts, S. G. B., & Dunbar, R. I. M. (2011). Use of social network sites and instant messaging does not lead to increased offline social network size, or to

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emotionally closer relationships with offline network members. CyberPsychology, Behavior and Social Networking, 14, 253-258. Rainie, L., Purcell, K & Smith, A. (2011). The social side of the Internet. Pew Internet & American Life Project. Sourander, A., Klomek, A. B., Ikonen, M., Lindroos, J., Luntamo, T., Koskelainen, et al. (2010). Psychosocial risk factors associated with cyberbullying among adolescents: A Population-Based Study. Archives of General Psychiatry, 67, 720-728. Tokunaga, R. S. (2010). Following you home from school: a critical review and synthesis of research on cyberbullying victimization. Computers in Human Behavior, 26, 277-87. Walrave, M., & Heirman, W. (2009) Cyberbullying: Predicting victimisation and perpetration. Children & Society, 25, 59-72. Warden, D., & MacKinnon, S. (2003). Prosocial children, bullies and victims: An investigation of their sociometric status, empathy and social problem-solving strategies. British Journal of Developmental Psychology, 21, 367–385. Ybarra M. L., Mitchell K. J., Wolak J., & Finkelhor D. (2006). Examining characteristics and associated distress related to Internet harassment: Findings from the second Youth Internet Safety Survey. Pediatrics, 118, 1169-1177.

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Table I Descriptive Statistics for Social Networking Site users Variable

M

SD

Age

13.98

0.88

Number of SNS used

2.46

1.23

Variable

n

%

Male

290

48.4

Female

309

51.6

1 day a week

28

4.7

2 to 3 days a week

80

13.4

4 to 5 days a week

120

20.0

6 to 7 days a week

371

61.9

I did not use SNS at school

259

43.2

Less than half an hour

221

36.9

About half an hour

42

7.0

About 1 hour

39

6.5

About 2 hours

14

2.3

About 3 hours

9

1.5

About 4 hours

15

2.5

I did not use SNS before or after school

16

2.7


138

23.0

Sex

Number of days used SNS a week

Number of hours used SNS at school

Number of hours used SNS before and after school

19

About half an hour

95

15.9

About 1 hour

129

21.5

About 2 hours

101

16.9

About 3 hours

52

8.7

About 4 hours

68

11.4

I did not use SNS on the weekend

1

0.2


61

10.2

About half an hour

53

8.8

About 1 hour

119

19.9

About 2 hours

126

21.0

About 3 hours

94

15.7

About 4 hours

145

24.2

0 to 19

8

1.3

20 to 49

18

3.0

50 to 99

35

5.8

100 to 149

59

9.8

150 to 199

61

10.2

200 to 299

124

20.7

300 or more

294

49.1

None

2

0.3

About half

46

7.7

Nearly all

337

56.3

Number of hours used SNS on the weekend

Number of SNS friends/contacts

Proportion of SNS friends/contacts known in person

20

All

214

35.7

Note. The Depression and Anxiety scores can range from 0 to 21, and the Total difficulties score can range from 0 to 40.

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Table II Descriptive Statistics for Mental health and Behavioural Functioning Variable

M

SD

Depression score

3.50

4.57

Anxiety score

2.72

3.31

Total difficulties score

11.28

5.91

n

%

Not exposed

426

71.1

Exposed

173

28.9

Variable Exposure to cyber-victimisation

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Table III Summary of Logistic Regression Analysis for Exposure to Cyber-Victimisation Variable

B

SE

Odds ratio

Wald statistic

Number of hours used SNS on the

0.27

0.07

1.32

16.71***

weekend

-0.31

0.11

0.74

7.91**

Age

0.15

0.07

1.16

4.49*


0.17

0.09

1.18

3.89*

Number of SNS used Note. *p < .05, **p < .01, ***p < .001.

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Table IV Regression Analysis Summary for Depression and Anxiety Scores B

SEB

β

Sex

1.53

0.37

0.17***


0.46

0.12

0.16***

Number of SNS used

0.47

0.17

0.12**

Sex

0.90

0.27

0.14**

Number of SNS used

0.36

0.12

0.12**


0.20

0.09

0.10*

Variable Depression score

Anxiety score

Note. *p < .05,**p < .01, ***p < .001.

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Table V Regression Analysis Summary for Total Difficulties Score B

SEB

β

Number of SNS used

0.98

0.22

0.19***


0.45

0.15

0.12**

Sex

1.30

0.47

0.11**


0.42

0.18

0.10*

Proportion of SNS friends/contacts known in

-0.88

0.38

-0.9*

Variable

person Note. *p < .05,**p < .01, ***p < .001.

25