EEMCS EPrints Service
Dadvar, M. and de Jong, F.M.G. and Ordelman, R.J.F. and Trieschnigg, R.B. (2012) Improved cyberbullying detection using gender information. In: Proceedings of the Twelfth Dutch-Belgian Information Retrieval Workshop (DIR 2012), 23-24 Feb 2012, Ghent, Belgium. pp. 23-25. University of Ghent . ISBN not assigned
Full text available as:
As a result of the invention of social networks, friendships, relationships and social communication are all undergoing changes and new definitions seem to be applicable. One may have hundreds of ‘friends’ without even seeing their faces. Meanwhile, alongside this transition there is increasing evidence that online social applications are used by children and adolescents for bullying. State-of-the-art studies in cyberbullying detection have mainly focused on the content of the conversations while largely ignoring the characteristics of the actors involved in cyberbullying. Social studies on cyberbullying reveal that the written language used by a harasser varies with the author’s features including gender. In this study we used a support vector machine model to train a gender-specific text classifier. We demonstrated that taking gender-specific language features into account improves the discrimination capacity of a classifier to detect cyberbullying.
Export this item as:
To correct this item please ask your editor
Repository Staff Only: edit this item