Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies

  • Maral Dadvar
  • Dolf Trieschnigg
  • Franciska de Jong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8436)


Cyberbullying is becoming a major concern in online environments with troubling consequences. However, most of the technical studies have focused on the detection of cyberbullying through identifying harassing comments rather than preventing the incidents by detecting the bullies. In this work we study the automatic detection of bully users on YouTube. We compare three types of automatic detection: an expert system, supervised machine learning models, and a hybrid type combining the two. All these systems assign a score indicating the level of “bulliness” of online bullies. We demonstrate that the expert system outperforms the machine learning models. The hybrid classifier shows an even better performance.


Support Vector Machine Expert System Person Pronoun Machine Learning Model Bully User 
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.
    Smith, P.K., et al.: Cyberbullying: Its nature and impact in secondary school pupils. Journal of Child Psychology and Psychiatry 49(4), 376–385 (2008)CrossRefGoogle Scholar
  2. 2.
    Perren, S., et al.: Coping with Cyberbullying:ASystematic Literature Review. Final Report of the ‘COST’IS 0801’ (2012)Google Scholar
  3. 3.
    Campbell, M.A.: Cyber bullying: An old problem in a new guise? Australian Journal of Guidance and Counselling 15(1), 68–76 (2005)CrossRefGoogle Scholar
  4. 4.
    Dinakar, K., Reichart, R., Lieberman, H.: Modeling the Detection of Textual Cyberbullying. In: Social Mobile Web Workshop at International Conference on Weblog and Social Media (2011)Google Scholar
  5. 5.
    Yin, D., et al.: 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
  6. 6.
    Dadvar, M., Trieschnigg, D., Ordelman, R., de Jong, F.: Improving cyberbullying detection with user context. In: Serdyukov, P., Braslavski, P., Kuznetsov, S.O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., Yilmaz, E. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 693–696. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Argamon, S., et al.: Mining the Blogosphere: Age, gender and the varieties of self-expression. First Monday 12(9) (2007)Google Scholar
  8. 8.
    Pazienza, M.T., Tudorache, A.G.: Interdisciplinary contributions to flame modeling. In: Pirrone, R., Sorbello, F. (eds.) AI*IA 2011. LNCS (LNAI), vol. 6934, pp. 213–224. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Dadvar, M., De Jong, F., Trieschnigg, D.: Expert knowledge for automatic detection of bullies in social networks. In: The 25th Benelux Conference on Artificial Intelligence (BNAIC 2013), Delft (2013)Google Scholar
  10. 10.
    Cha, M., et al.: I tube, you tube, everybody tubes: Analyzing the world’s largest user generated content video system. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement. ACM (2007)Google Scholar
  11. 11.
    Figueira, J., Greco, S., Ehrgott, M.: Multiple criteria decision analysis: State of the art surveys, vol. 78. Springer (2005)Google Scholar
  12. 12.
    Farah, M., Vanderpooten, D.: A multiple criteria approach for information retrieval. In: Crestani, F., Ferragina, P., Sanderson, M. (eds.) SPIRE 2006. LNCS, vol. 4209, pp. 242–254. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  13. 13.
    Xu, Z., Khoshgoftaar, T.M., Allen, E.B.: Application of fuzzy expert systems in assessing operational risk of software. Information and Software Technology 45(7), 373–388 (2003)CrossRefGoogle Scholar
  14. 14.
    Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques. Elsevier (2011)Google Scholar
  15. 15.
    Hall, M., et al.: The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)CrossRefGoogle Scholar
  16. 16.
    Fielding, A.H., Bell, J.F.: A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24(1), 38–49 (1997)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

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

Personalised recommendations