Towards User Modelling in the Combat against Cyberbullying

  • Maral Dadvar
  • Roeland Ordelman
  • Franciska de Jong
  • Dolf Trieschnigg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7337)


Friendships, relationships and social communications have all gone to a new level with new definitions as a result of the invention of online social networks. Meanwhile, alongside this transition there is increasing evidence that online social applications have been used by children and adolescents for bullying. State-of-the-art studies in cyberbullying detection have mainly focused on the content of the conversations while largely ignoring the users involved in cyberbullying. We hypothesis that incorporation of the users’ profile, their characteristics, and post-harassing behaviour, for instance, posting a new status in another social network as a reaction to their bullying experience, will improve the accuracy of cyberbullying detection. Cross-system analyses of the users’ behaviour - monitoring users’ reactions in different online environments - can facilitate this process and could lead to more accurate detection of cyberbullying. This paper outlines the framework for this faceted approach.


Online Social Network Supervise Learning Approach Online Bully Social Networking Graph Multiclass Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Campbell, M.A.: Cyber bullying: An old problem in a new guise? Australian Journal of Guidance and Counselling 15, 68–76 (2005)CrossRefGoogle Scholar
  2. 2.
    Espelage, D.L., Swearer, S.M.: Research on school bullying and victimization. School Psychology Review 32, 365–383 (2003)Google Scholar
  3. 3.
    Smith, P.K., 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 49, 376–385 (2008)CrossRefGoogle Scholar
  4. 4.
    Kowalski, R.M., Limber, S.P., Agatston, P.W.: Cyber bullying: Bullying in the digital age, p. 224. Blackwell Publishing (2008)Google Scholar
  5. 5.
    Yin, D., Xue, Z., Hong, L., Davison, B.D., Kontostathis, A., Edwards, L.: Detection of harassment on Web 2.0. In: Proceedings of CAW2.0, Madrid, April 20-24 (2009)Google Scholar
  6. 6.
    Dinakar, K., Reichart, R., Lieberman, H.: Modelling the Detection of Textual Cyberbullying. In: ICWSM 2011, Barcelona, Spain, July 17-21 (2011)Google Scholar
  7. 7.
    Kontostathis, A.: ChatCoder: Toward the tracking and categorization of internet predators. In: Proceedings of SDM 2009, Sparks, NV, May 2 (2009)Google Scholar
  8. 8.
    Tan, P.N., Chen, F., Jain, A.: Information assurance: Detection of web spam attacks in social media. In: Proceedings of Army Science Conference, Orland, Florida (2010)Google Scholar
  9. 9.
    Chisholm, J.F.: Cyberspace violence against girls and adolescent females. Annals of the New York Academy of Sciences 1087, 74–89 (2006)CrossRefGoogle Scholar
  10. 10.
    Carmagnola, F., Cena, F.: User identification for cross-system personalisation. Information Sciences 179, 16–32 (2009)CrossRefGoogle Scholar
  11. 11.
    Abel, F., Araújo, S., Gao, Q., Houben, G.-J.: Analyzing Cross-System User Modeling on the Social Web. In: Auer, S., Díaz, O., Papadopoulos, G.A. (eds.) ICWE 2011. LNCS, vol. 6757, pp. 28–43. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Argamon, S., Koppel, M., Fine, J., Shimoni, A.R.: Gender, genre, and writing style in formal written texts. Text - Interdisciplinary Journal for the Study of Discourse 23, 321–346 (2003)CrossRefGoogle Scholar
  13. 13.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Newsletter 11, 10–18 (2009)CrossRefGoogle Scholar
  14. 14.
    Abel, F., Henze, N., Herder, E., Krause, D.: Linkage, aggregation, alignment and enrichment of public user profiles with Mypes. In: Proceedings of I-SEMANTICS, Graz, Austria, pp. 1–8 (September 2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Maral Dadvar
    • 1
  • Roeland Ordelman
    • 1
  • Franciska de Jong
    • 1
  • Dolf Trieschnigg
    • 2
  1. 1.Human Media Interaction GroupUniversity of TwenteEnschedeThe Netherlands
  2. 2.Database GroupUniversity of TwenteEnschedeThe Netherlands

Personalised recommendations