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On cyberbullying incidents and underlying online social relationships

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Abstract

Cyberbullying is an important social challenge that takes place over a technical substrate. Thus, it has attracted research interest across both computational and social science research communities. While the social science studies conducted via careful participant selection have shown the effect of personality, social relationships, and psychological factors on cyberbullying, they are often limited in scale due to manual survey or ethnographic study components. Computational approaches on the other hand have defined multiple automated approaches for detecting cyberbullying at scale, and have largely focused only on the textual content of the messages exchanged. There are no existing efforts aimed at testing, validating, and potentially refining the findings from traditional bullying literature as obtained via surveys and ethnographic studies at scale over online environments. By analyzing the social relationship graph between users in an online social network and deriving features such as out-degree centrality and the number of common friends, we find that multiple social characteristics are statistically different between the cyberbullying and non-bullying groups, thus supporting many, but not all, of the results found in previous survey-based bullying studies. The results pave way for better understanding of the cyberbullying phenomena at scale.

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References

  1. Smith, P. K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S., & Tippett, N. (2008). Cyberbullying: Its nature and impact in secondary school pupils. Journal of Child Psychology and Psychiatry, 49(4), 376–385.

    Article  Google Scholar 

  2. Hinduja, S., & Patchin, J. W. (2008). Cyberbullying: An exploratory analysis of factors related to offending and victimization. Deviant Behavior, 29(2), 129–156.

    Article  Google Scholar 

  3. Cyberbullying Research Center. (2015). Cyberbullying Data. https://cyberbullying.org/2015-data. Accessed 21 July 2018.

  4. Sourander, A., Klomek, A. B., Ikonen, M., Lindroos, J., Luntamo, T., Koskelainen, M., et al. (2010). Psychosocial risk factors associated with cyberbullying among adolescents: A population-based study. Archives of General Psychiatry, 67(7), 720–728.

    Article  Google Scholar 

  5. BBC News. (2010). ’Bullying’ link to child suicide rate, charity suggests. https://www.bbc.co.uk/news/10302550. Accessed 21 July 2018.

  6. Hinduja, S., & Patchin, J. W. (2010). Bullying, cyberbullying, and suicide. Archives of Suicide Research, 14(3), 206–221.

    Article  Google Scholar 

  7. Kowalski, R. M., & Limber, S. P. (2007). Electronic bullying among middle school students. Journal of Adolescent Health, 41(6), S22–S30.

    Article  Google Scholar 

  8. Mason, K. L. (2008). Cyberbullying: A preliminary assessment for school personnel. Psychology in the Schools, 45(4), 323–348.

    Article  Google Scholar 

  9. Patchin, J. W., & Hinduja, S. (2010). Cyberbullying and self-esteem. Journal of School Health, 80(12), 614–621.

    Article  Google Scholar 

  10. Boyd, D., Marwick, A., Aftab, P., & Koeltl, M. The conundrum of visibility: Youth safety and the internet.

  11. Crick, N. R., & Nelson, D. A. (2002). Relational and physical victimization within friendships: Nobody told me there’d be friends like these. Journal of Abnormal Child Psychology, 30(6), 599–607.

    Article  Google Scholar 

  12. Huang, Q., Singh, V. K., & Atrey, P. K. (2014). Cyber bullying detection using social and textual analysis, in: Proceedings of the 3rd International Workshop on Socially-Aware Multimedia, SAM ’14, ACM, New York, NY, USA, 2014, pp. 3–6. 10.1145/2661126.2661133. http://doi.acm.org/10.1145/2661126.2661133

  13. Squicciarini, A., Rajtmajer, S., Liu, Y., & Griffin, C. (2015). Identification and characterization of cyberbullying dynamics in an online social network, in: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, ACM, 2015, pp. 280–285.

  14. Qiu, L., Chan, S. H. M., & Chan, D. (2018). Big data in social and psychological science: theoretical and methodological issues. Journal of Computational Social Science, 1(1), 59–66.

    Article  Google Scholar 

  15. Slonje, R., & Smith, P. K. (2008). Cyberbullying: Another main type of bullying? Scandinavian Journal of Psychology, 49(2), 147–154.

    Article  Google Scholar 

  16. Tokunaga, R. S. (2010). Following you home from school: A critical review and synthesis of research on cyberbullying victimization. Computers in Human Behavior, 26(3), 277–287.

    Article  Google Scholar 

  17. Festl, R., & Quandt, T. (2013). Social relations and cyberbullying: The influence of individual and structural attributes on victimization and perpetration via the internet. Human Communication Research, 39(1), 101–126.

    Article  Google Scholar 

  18. Wang, J., Iannotti, R. J., & Luk, J. W. (2010). Bullying victimization among underweight and overweight us youth: Differential associations for boys and girls. Journal of Adolescent Health, 47(1), 99–101.

    Article  Google Scholar 

  19. Olweus, D. (1997). Bully/victim problems in school: Facts and intervention. European Journal of Psychology of Education, 12(4), 495–510.

    Article  Google Scholar 

  20. Mishna, F., Saini, M., & Solomon, S. (2009). Ongoing and online: Children and youth’s perceptions of cyber bullying. Children and Youth Services Review, 31(12), 1222–1228.

    Article  Google Scholar 

  21. Campbell, M., Spears, B., Slee, P., Butler, D., & Kift, S. (2012). Victims perceptions of traditional and cyberbullying, and the psychosocial correlates of their victimisation. Emotional and Behavioural Difficulties, 17(3–4), 389–401.

    Article  Google Scholar 

  22. Navarro, R., Yubero, S., Larrañaga, E., & Martínez, V. (2012). Childrens cyberbullying victimization: associations with social anxiety and social competence in a spanish sample. Child Indicators Research, 5(2), 281–295.

    Article  Google Scholar 

  23. Minkkinen, J. (2013). Associations between school-related factors and depressive symptoms among children: a comparative study, Finland and Norway, School Psychology International. 0143034313511008.

  24. Beran, T., & Li, Q. (2008). The relationship between bullying and school bullying. The Journal of Student Wellbeing, 1(2), 16–33.

    Article  Google Scholar 

  25. Mishna, F., Khoury-Kassabri, M., Gadalla, T., & Daciuk, J. (2012). Risk factors for involvement in cyber bullying: Victims, bullies and bully-victims. Children and Youth Services Review, 34(1), 63–70.

    Article  Google Scholar 

  26. Reynolds, K., Kontostathis, A., & Edwards, L. (2011). Using machine learning to detect cyberbullying, in: Machine Learning and Applications and Workshops (ICMLA), 2011 10th International Conference on, Vol. 2, IEEE, 2011, pp. 241–244.

  27. Dinakar, K., Reichart, R., & Lieberman, H. (2011). Modeling the detection of textual cyberbullying., in: The Social Mobile Web.

  28. Dinakar, K., Jones, B., Havasi, C., Lieberman, H., & Picard, R. (2012). Common sense reasoning for detection, prevention, and mitigation of cyberbullying. ACM Transactions on Interactive Intelligent Systems (TiS), 2(3), 18.

    Google Scholar 

  29. Dadvar, M. de Jong, E., Ordelman, R., & Trieschnigg, R. Improved cyberbullying detection using gender information.

  30. Bigelow, J. L., Edwards, L., et al. (2016). Detecting cyberbullying using latent semantic indexing, in: Proceedings of the First International Workshop on Computational Methods for CyberSafety, ACM, pp. 11–14.

  31. Zhao, R., & Mao, K. Cyberbullying detection based on semantic-enhanced marginalized denoising auto-encoder. IEEE Transactions on Affective Computing.

  32. Nahar, V., Li, X., & Pang, C. An effective approach for cyberbullying detection, Communications in Information Science and Management Engineering.

  33. Nahar, V., Unankard, S., Li, X., & Pang, C. (2012). Sentiment analysis for effective detection of cyber bullying. Web Technologies and Applications. Springer, pp. 767–774.

  34. Hosseinmardi, H., Mattson, S. A., Rafiq, R. I., Han, R., Lv, Q., & Mishra, S. Detection of cyberbullying incidents on the instagram social network. arXiv preprint arXiv:1503.03909.

  35. Hosseinmardi, H., Mattson, S. A., Rafiq, R. I., Han, R., Lv, Q., & Mishra, S. (2015). Analyzing labeled cyberbullying incidents on the instagram social network, in: International Conference on Social Informatics, Springer, pp. 49–66.

  36. Hosseinmardi, H., Rafiq, R. I.., Han, R, Lv, Q., & Mishra, S. (2016). Prediction of cyberbullying incidents in a media-based social network, in: Advances in Social Networks Analysis and Mining (ASONAM), IEEE/ACM International Conference on, IEEE, 2016, pp. 186–192.

  37. Wulczyn, E., Thain, N., & Dixon, L. (2017). Ex machina: Personal attacks seen at scale, in: Proceedings of the 26th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, pp. 1391–1399.

  38. Chu, T., Jue, K., & Wang, M. Comment abuse classification with deep learning.

  39. Xu, J.-M., Huang, H.-C., Bellmore, A., & Zhu, X. (2014). School bullying in twitter and weibo: a comparative study. Reporter, 7(16), 10–14.

    Google Scholar 

  40. Cortis, K., & Handschuh, S. (2015) Analysis of cyberbullying tweets in trending world events, in: Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business, i-KNOW ’15, ACM, New York, NY, USA, 2015, pp. 7:1–7:8. 10.1145/2809563.2809605. http://doi.acm.org/10.1145/2809563.2809605.

  41. Campfield, D. C. (2008). Bullying and victimization: Psychosocial characteristics of bullies, victims, and bully/victims, Graduate Student Theses, Dissertations, and Professional Papers (University of Montana). ProQuest, 2008.

  42. Salmivalli, C., Huttunen, A., & Lagerspetz, K. M. (1997). Peer networks and bullying in schools. Scandinavian Journal of Psychology, 38(4), 305–312.

    Article  Google Scholar 

  43. Xie, H., Swift, D. J., Cairns, B. D., & Cairns, R. B. (2002). Aggressive behaviors in social interaction and developmental adaptation: A narrative analysis of interpersonal conflicts during early adolescence. Social Development, 11(2), 205–224.

    Article  Google Scholar 

  44. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879.

    Article  Google Scholar 

  45. Bless, H., Fiedler, K., & Strack, F. (2004) Social cognition: How individuals construct social reality. Psychology Press.

  46. Eagle, N., & Pentland, A. (2006). Reality mining: sensing complex social systems. Personal and Ubiquitous Computing, 10(4), 255–268.

    Article  Google Scholar 

  47. Singh, V. K., & Jain, A. (2017) Toward harmonizing self-reported and logged social data for understanding human behavior, in: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, ACM, pp. 2233–2238.

  48. Giles, J. (2012). Making the links. Nature, 488(7412), 448–450.

    Article  Google Scholar 

  49. Brighi, A., Melotti, G., Guarini, A., Genta, M. L., Ortega, R., & Mora-Merchán, J., et al. (2012). Self-esteem and Loneliness in Relation to Cyberbullying in Three European Countries. Cyberbullying in the Global Playground: Research from International Perspectives (pp. 32–56).

  50. Reis, H. T., & Sprecher, S. (2009). Encyclopedia of Human Relationships (Vol. 1). Thousand Oaks: Sage.

    Book  Google Scholar 

  51. Cazenave, N. A., & Straus, M. A. (1979). Race, class, network embeddedness and family violence: A search for potent support systems. Journal of Comparative Family Studies, 10, 281–300.

    Google Scholar 

  52. Tong, S. T., Van Der Heide, B., Langwell, L., & Walther, J. B. (2008). Too much of a good thing? the relationship between number of friends and interpersonal impressions on facebook. Journal of Computer-Mediated Communication, 13(3), 531–549.

    Article  Google Scholar 

  53. Raghavan, V., Ver Steeg, G., Galstyan, A., & Tartakovsky, A. G. (2013) Modeling temporal activity patterns in dynamic social networks, Computational Social Systems, IEEE Transactions on 1.

  54. Casey-Cannon, S., Hayward, C., & Gowen, K. (2001) Middle-school girls’ reports of peer victimization: Concerns, consequences, and implications., Professional School Counseling.

  55. Mishna, F., Wiener, J., & Pepler, D. (2008). Some of my best friends experiences of bullying within friendships. School Psychology International, 29(5), 549–573.

    Article  Google Scholar 

  56. Mishna, F., Cook, C., Gadalla, T., Daciuk, J., & Solomon, S. (2010). Cyber bullying behaviors among middle and high school students. American Journal of Orthopsychiatry, 80(3), 362–374.

    Article  Google Scholar 

  57. Adamic, L. A., & Adar, E. (2003). Friends and neighbors on the web. Social Networks, 25(3), 211–230.

    Article  Google Scholar 

  58. Gilbert, G., & Karahalios, K. (2009) Predicting tie strength with social media, in: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, pp. 211–220.

  59. Olweus, D. (1980). Familial and temperamental determinants of aggressive behavior in adolescent boys: A causal analysis. Developmental Psychology, 16(6), 644.

    Article  Google Scholar 

  60. Morahan-Martin, J., & Schumacher, P. (2003). Loneliness and social uses of the internet. Computers in Human Behavior, 19(6), 659–671.

    Article  Google Scholar 

  61. Newman, M. (2010). Networks: An introduction. Oxford: Oxford University Press.

    Book  Google Scholar 

  62. Altshuler, Y., Fire, M., Shmueli, E., Elovici, Y., Bruckstein, A., Pentland, A. S., et al. (2013). The social amplifier reaction of human communities to emergencies. Journal of Statistical Physics, 152(3), 399–418.

    Article  Google Scholar 

  63. Dávid-Barrett, T., & Dunbar, R. (2013). Processing power limits social group size: Computational evidence for the cognitive costs of sociality. Proceedings of the Royal Society B: Biological Sciences, 280(1765), 20131151.

    Article  Google Scholar 

  64. Gupte, M., & Eliassi-Rad, T. (2012) Measuring tie strength in implicit social networks, in: Proceedings of the 3rd Annual ACM Web Science Conference, ACM, pp. 109–118.

  65. Steinfield, C., Ellison, N. B., & Lampe, C. (2008). Social capital, self-esteem, and use of online social network sites: A longitudinal analysis. Journal of Applied Developmental Psychology, 29(6), 434–445.

    Article  Google Scholar 

  66. Walker, K. N., MacBride, A., & Vachon, M. L. (1977). Social support networks and the crisis of bereavement. Social Science & Medicine, 11(1), 35–41.

    Article  Google Scholar 

  67. Lin, N. (1999). Building a network theory of social capital. Connections, 22(1), 28–51.

    Google Scholar 

  68. Lopez, C., & DuBois, D. L. (2005). Peer victimization and rejection: Investigation of an integrative model of effects on emotional, behavioral, and academic adjustment in early adolescence. Journal of Clinical Child and Adolescent Psychology, 34(1), 25–36.

    Article  Google Scholar 

  69. Nahar, V., Al-Maskari, S., Li, X., & Pang, C. (2014) Semi-supervised learning for cyberbullying detection in social networks, in: Australasian Database Conference, Springer, pp. 160–171.

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Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

    Qianjia Huang

  2. School of Communication and Information, Rutgers, The State University of New Jersey and Media Lab, Massachusetts Institute of Technology, Cambridge, USA

    Vivek K. Singh

  3. Department of Computer Science, University at Albany, State University of New York, Albany, USA

    Pradeep K. Atrey

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  1. Qianjia Huang
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Correspondence to Vivek K. Singh.

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Huang, Q., Singh, V.K. & Atrey, P.K. On cyberbullying incidents and underlying online social relationships. J Comput Soc Sc 1, 241–260 (2018). https://doi.org/10.1007/s42001-018-0026-9

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