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Automatic detection and prevention of cyberbullying

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Abstract
The recent development of social media poses new challenges to the research community in analyzing online interactions between people. Social networking sites offer great opportunities for connecting with others, but also increase the vulnerability of young people to undesirable phenomena, such as cybervictimization. Recent research reports that on average, 20% to 40% of all teenagers have been victimized online. In this paper, we focus on cyberbullying as a particular form of cybervictimization. Successful prevention depends on the adequate detection of potentially harmful messages. However, given the massive information overload on the Web, there is a need for intelligent systems to identify potential risks automatically. We present the construction and annotation of a corpus of Dutch social media posts annotated with fine-grained cyberbullying-related text categories, such as insults and threats. Also, the specific participants (harasser, victim or bystander) in a cyberbullying conversation are identified to enhance the analysis of human interactions involving cyberbullying. Apart from describing our dataset construction and annotation, we present proof-of-concept experiments on the automatic identification of cyberbullying events and fine-grained cyberbullying categories.
Keywords
text classification, dataset construction, cyberbullying prevention

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MLA
Van Hee, Cynthia et al. “Automatic Detection and Prevention of Cyberbullying.” International Conference on Human and Social Analytics, Proceedings. Ed. Pascal Lorenz & Christian Bourret. IARIA, 2015. 13–18. Print.
APA
Van Hee, C., Lefever, E., Verhoeven, B., Mennes, J., Desmet, B., De Pauw, G., Daelemans, W., et al. (2015). Automatic detection and prevention of cyberbullying. In P. Lorenz & C. Bourret (Eds.), International Conference on Human and Social Analytics, Proceedings (pp. 13–18). Presented at the International Conference on Human and Social Analytics (HUSO 2015), IARIA.
Chicago author-date
Van Hee, Cynthia, Els Lefever, Ben Verhoeven, Julie Mennes, Bart Desmet, Guy De Pauw, Walter Daelemans, and Veronique Hoste. 2015. “Automatic Detection and Prevention of Cyberbullying.” In International Conference on Human and Social Analytics, Proceedings, ed. Pascal Lorenz and Christian Bourret, 13–18. IARIA.
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. “Automatic Detection and Prevention of Cyberbullying.” In International Conference on Human and Social Analytics, Proceedings, ed. Pascal Lorenz and Christian Bourret, 13–18. IARIA.
Vancouver
1.
Van Hee C, Lefever E, Verhoeven B, Mennes J, Desmet B, De Pauw G, et al. Automatic detection and prevention of cyberbullying. In: Lorenz P, Bourret C, editors. International Conference on Human and Social Analytics, Proceedings. IARIA; 2015. p. 13–8.
IEEE
[1]
C. Van Hee et al., “Automatic detection and prevention of cyberbullying,” in International Conference on Human and Social Analytics, Proceedings, Saint Julians, Malta, 2015, pp. 13–18.
@inproceedings{7010768,
  abstract     = {The recent development of social media poses new challenges to the research community in analyzing online interactions between people. Social networking sites offer great opportunities for connecting with others, but also increase the vulnerability of young people to undesirable phenomena, such as cybervictimization. Recent research reports that on average, 20% to 40% of all teenagers have been victimized online. In this paper, we focus on cyberbullying as a particular form of cybervictimization. Successful prevention depends on the adequate detection of potentially harmful messages. However, given the massive information overload on the Web, there is a need for intelligent systems to identify potential risks automatically. We present the construction and annotation of a corpus of Dutch social media posts annotated with fine-grained cyberbullying-related text categories, such as insults and threats. Also, the specific participants (harasser, victim or bystander) in a cyberbullying conversation are identified to enhance the analysis of human interactions involving cyberbullying. Apart from describing our dataset construction and annotation, we present proof-of-concept experiments on the automatic identification of cyberbullying events and fine-grained cyberbullying categories.},
  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    = {International Conference on Human and Social Analytics, Proceedings},
  editor       = {Lorenz, Pascal and Bourret, Christian},
  isbn         = {978-1-61208-447-3},
  keywords     = {text classification,dataset construction,cyberbullying prevention},
  language     = {eng},
  location     = {Saint Julians, Malta},
  pages        = {13--18},
  publisher    = {IARIA},
  title        = {Automatic detection and prevention of cyberbullying},
  year         = {2015},
}