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Automatic Detection of Cyberbullying in Social Media Text

(2018) PLOS ONE. 13.
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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.
Keywords
cyberbullying detection, machine learning, social media, text mining, lt3

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Chicago
Van Hee, Cynthia, Gilles Jacobs, Chris Emmery, Bart Desmet, Els Lefever, Ben Verhoeven, Guy De Pauw, Walter Daelemans, and Veronique Hoste. 2018. “Automatic Detection of Cyberbullying in Social Media Text.” Ed. Hussein Suleman. Plos One 13: 1–22.
APA
Van Hee, C., Jacobs, G., Emmery, C., Desmet, B., Lefever, E., Verhoeven, B., De Pauw, G., et al. (2018). Automatic Detection of Cyberbullying in Social Media Text. (H. Suleman, Ed.)PLOS ONE, 13, 1–22.
Vancouver
1.
Van Hee C, Jacobs G, Emmery C, Desmet B, Lefever E, Verhoeven B, et al. Automatic Detection of Cyberbullying in Social Media Text. Suleman H, editor. PLOS ONE. Public Library of Science; 2018;13:1–22.
MLA
Van Hee, Cynthia, Gilles Jacobs, Chris Emmery, et al. “Automatic Detection of Cyberbullying in Social Media Text.” Ed. Hussein Suleman. PLOS ONE 13 (2018): 1–22. Print.
@article{8573574,
  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.},
  articleno    = {10},
  author       = {Van Hee, Cynthia and Jacobs, Gilles and Emmery, Chris and Desmet, Bart and Lefever, Els and Verhoeven, Ben and De Pauw, Guy and Daelemans, Walter and Hoste, Veronique},
  editor       = {Suleman, Hussein},
  journal      = {PLOS ONE},
  keyword      = {cyberbullying detection,machine learning,social media,text mining,lt3},
  language     = {eng},
  pages        = {22},
  publisher    = {Public Library of Science},
  title        = {Automatic Detection of Cyberbullying in Social Media Text},
  url          = {http://dx.doi.org/10.1371/journal.pone.0203794},
  volume       = {13},
  year         = {2018},
}

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