Automatic Detection of Cyberbullying in Social Media Text

Cynthia Van Hee, Gilles Jacobs*, Chris Emmery, Bart Desmet, Els Lefever, Ben Verhoeven, Guy De Pauw, W.M.P. Daelemans, Veronique Hoste

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review


While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of potentially harmful messages and the information overload on the Web requires intelligent systems to identify potential risks automatically. The focus of this paper is on automatic cyberbullying detection in social media text by modelling posts written by bullies, victims, and bystanders of online bullying. We describe the collection and fine-grained annotation of a training corpus for English and Dutch and perform a series of binary classification experiments to determine the feasibility of automatic cyberbullying detection. We make use of linear support vector machines exploiting a rich feature set and investigate which information sources contribute the most for this particular task. Experiments on a holdout test set reveal promising results for the detection of cyberbullying-related posts. After optimisation of the hyperparameters, the classifier yields an F1-score of 64% and 61% for English and Dutch respectively, and considerably outperforms baseline systems based on keywords and word unigrams.
Original languageEnglish
Article numbere0203794
Number of pages21
Issue number10
Early online date17 Jan 2018
Publication statusPublished - 2018


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  • Automatic detection of cyberbullying in social media text

    Van Hee, C., Jacobs, G., Emmery, C., Desmet, B., Lefever, E., Verhoeven, B., De Pauw, G., Daelemans, W. M. P. & Hoste, V., 8 Oct 2018, In: PLOS ONE. 13, 10, p. e0203794 22 p., 0203794.

    Research output: Contribution to journalArticleScientificpeer-review

    Open Access
    64 Citations (Scopus)

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