Improving Cyberbullying Detection with User Context

  • Maral Dadvar
  • Dolf Trieschnigg
  • Roeland Ordelman
  • Franciska de Jong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7814)

Abstract

The negative consequences of cyberbullying are becoming more alarming every day and technical solutions that allow for taking appropriate action by means of automated detection are still very limited. Up until now, studies on cyberbullying detection have focused on individual comments only, disregarding context such as users’ characteristics and profile information. In this paper we show that taking user context into account improves the detection of cyberbullying.

References

  1. 1.
    Espelage, D.L., Swearer, S.M.: Research on school bullying and victimization: What have we learned and where do we go from here? School Psychology Review 32(3), 365–383 (2003)Google Scholar
  2. 2.
    Yin, D., Xue, Z., Hong, L., Davison, B.D., Kontostathis, A., Edwards, L.: Detection of Harassment on Web 2.0. In: Proceedings of the Content Analysis in the WEB 2.0 (CAW2.0) Workshop at WWW 2009, Madrid, Spain (2009)Google Scholar
  3. 3.
    Dinakar, K., Reichart, R., Lieberman, H.: Modeling the detection of textual cyberbullying. In: International Conference on Weblog and Social Media - Social Mobile Web Workshop, Barcelona, Spain (2011)Google Scholar
  4. 4.
    Dadvar, M., de Jong, F.M.G., Ordelman, R.J.F., Trieschnigg, D.: Improved Cyberbullying Detection Using Gender Information. In: Proceedings of the Twelfth Dutch-Belgian Information Retrieval Workshop (DIR 2012), Ghent, Belgium, pp. 23–26 (2012)Google Scholar
  5. 5.
    Sood, S., Antin, J., Churchill, E.: Using Crowdsourcing to Improve Profanity Detection. In: AAAI Spring Symposium Series, pp. 69–74 (2012)Google Scholar
  6. 6.
    Kontostathis, A., Leatherman, L.E.A.: ChatCoder: Toward the tracking and categorization of internet predators. In: Proceedings of Text Mining Workshop 2009 held in Conjunction with the Ninth SIAM International Conference on Data Mining, Nevada, USA (2009)Google Scholar
  7. 7.
    Chen, Y., Zhu, S., Zhou, Y., Xu, H.: Detecting Offensive Language in Social Media to Protect Adolescent Online Safety. In: Symposium on Usable Privacy and Security, Pittsburgh, USA (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

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

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