Detection and fine-grained classification of cyberbullying events

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Van Hee, C., Lefever, E., Verhoeven, B., Mennes, J., Desmet, B., De Pauw, G., Daelemans, W., & Hoste, V.
Galia Angelova, Kalina Bontcheva and Ruslan Mitkov
Proceedings of Recent Advances in Natural Language Processing, Proceedings
International Conference Recent Advances in Natural Language Processing (RANLP) (Hissar, Bulgaria)
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In the current era of online interactions, both positive and negative experiences are abundant on the Web. As in real life, negative experiences can have a serious impact on youngsters. Recent studies have reported cybervictimization rates among teenagers that vary between 20% and 40%. In this paper, we focus on cyberbullying as a particular form of cybervictimization and explore its automatic detection and fine-grained classification. Data containing cyberbullying was collected from the social networking site We developed and applied a new scheme for cyberbullying annotation, which describes the presence and severity of cyberbullying, a post author's role (harasser, victim or bystander) and a number of fine-grained categories related to cyberbullying, such as insults and threats. We present experimental results on the automatic detection of cyberbullying and explore the feasibility of detecting the more fine-grained cyberbullying categories in online posts. For the first task, an F-score of 55.39% is obtained. We observe that the detection of the fine-grained categories (e.g. threats) is more challenging, presumably due to data sparsity, and because they are often expressed in a subtle and implicit way.