Automatic detection of cyberbullying in social media text

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

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

42 Citations (Scopus)

Abstract

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 cyberbullying 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 the task. Experiments on a hold-out 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.
Original languageEnglish
Article number0203794
Pages (from-to)e0203794
Number of pages22
JournalPLoS ONE
Volume13
Issue number10
Early online dateJan 2018
DOIs
Publication statusPublished - 8 Oct 2018

Keywords

  • BULLYING BEHAVIOR
  • EXPERIENCES
  • HEALTH
  • IMPACT
  • NETWORK
  • YOUNGSTERS

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