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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
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)
Sood, S., Antin, J., Churchill, E.: Using Crowdsourcing to Improve Profanity Detection. In: AAAI Spring Symposium Series, pp. 69–74 (2012)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dadvar, M., Trieschnigg, D., Ordelman, R., de Jong, F. (2013). Improving Cyberbullying Detection with User Context. In: Serdyukov, P., et al. Advances in Information Retrieval. ECIR 2013. Lecture Notes in Computer Science, vol 7814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36973-5_62
Download citation
DOI: https://doi.org/10.1007/978-3-642-36973-5_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36972-8
Online ISBN: 978-3-642-36973-5
eBook Packages: Computer ScienceComputer Science (R0)