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Detection and fine-grained classification of cyberbullying events

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Abstract
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 Ask.fm. 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.
Keywords
cyberbullying, classification, natural language processing

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MLA
Van Hee, Cynthia, et al. “Detection and Fine-Grained Classification of Cyberbullying Events.” Proceedings of Recent Advances in Natural Language Processing, Proceedings, edited by Galia Angelova et al., 2015, pp. 672–80.
APA
Van Hee, C., Lefever, E., Verhoeven, B., Mennes, J., Desmet, B., De Pauw, G., … Hoste, V. (2015). Detection and fine-grained classification of cyberbullying events. In G. Angelova, K. Bontcheva, & R. Mitkov (Eds.), Proceedings of Recent Advances in Natural Language Processing, Proceedings (pp. 672–680). Hissar, Bulgaria.
Chicago author-date
Van Hee, Cynthia, Els Lefever, Ben Verhoeven, Julie Mennes, Bart Desmet, Guy De Pauw, Walter Daelemans, and Veronique Hoste. 2015. “Detection and Fine-Grained Classification of Cyberbullying Events.” In Proceedings of Recent Advances in Natural Language Processing, Proceedings, edited by Galia Angelova, Kalina Bontcheva, and Ruslan Mitkov, 672–80.
Chicago author-date (all authors)
Van Hee, Cynthia, Els Lefever, Ben Verhoeven, Julie Mennes, Bart Desmet, Guy De Pauw, Walter Daelemans, and Veronique Hoste. 2015. “Detection and Fine-Grained Classification of Cyberbullying Events.” In Proceedings of Recent Advances in Natural Language Processing, Proceedings, ed by. Galia Angelova, Kalina Bontcheva, and Ruslan Mitkov, 672–680.
Vancouver
1.
Van Hee C, Lefever E, Verhoeven B, Mennes J, Desmet B, De Pauw G, et al. Detection and fine-grained classification of cyberbullying events. In: Angelova G, Bontcheva K, Mitkov R, editors. Proceedings of Recent Advances in Natural Language Processing, Proceedings. 2015. p. 672–80.
IEEE
[1]
C. Van Hee et al., “Detection and fine-grained classification of cyberbullying events,” in Proceedings of Recent Advances in Natural Language Processing, Proceedings, Hissar, Bulgaria, 2015, pp. 672–680.
@inproceedings{6969774,
  abstract     = {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 Ask.fm. 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.},
  author       = {Van Hee, Cynthia and Lefever, Els and Verhoeven, Ben and Mennes, Julie and Desmet, Bart and De Pauw, Guy and Daelemans, Walter and Hoste, Veronique},
  booktitle    = {Proceedings of Recent Advances in Natural Language Processing, Proceedings},
  editor       = {Angelova, Galia and Bontcheva, Kalina and Mitkov, Ruslan},
  issn         = {1313-8502},
  keywords     = {cyberbullying,classification,natural language processing},
  language     = {eng},
  location     = {Hissar, Bulgaria},
  pages        = {672--680},
  title        = {Detection and fine-grained classification of cyberbullying events},
  year         = {2015},
}