Automatic detection of cyberbullying in social media text

By Cynthia Van Hee, Gilles Jacobs, Chris Emmery, Bart Desmet, Els Lefever, Ben Verhoeven, Guy De Pauw, Walter Daelemans, Veronique Hoste. PLoS ONE, 2018

333 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Social Psychology

View Paper: https://www.semanticscholar.org/paper/beb0e2682cb0ac132dc348044d1940669fb7194a

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Citations in the corpus

Civil Rephrases Of Toxic Texts With Self-Supervised Transformers (2021, background): “…its strategy may involve shifting the original intent, since “republican” is not a non-offensive synonym of “moron”. et al., 2017; Pavlopoulos et al., 2017; Wulczyn et al., 2017; Gamb¨ack and Sikdar, 2017; Fortuna and Nunes, 2018; Zhang et al., 2018a; Van Hee et al., 2018; Zampieri et al., 2019).”
ETHOS: a multi-label hate speech detection dataset (2020, methodology): “The issue of cyberbullying has been recently investigated too, where the skewed distribution of positive and negative comments was tackled by tuning a cost-sensitive linear SVM learner over various combinations of joined feature spaces andobtaining similar performanceonbothEnglish andDutch corpus [5”
Misogyny Detection in Twitter: a Multilingual and Cross-Domain Study (2020, neutral): “However, they could also lead to false positives when the swear word is used in a casual, not offensive context Malmasi and Zampieri (2018); Nobata, Tetreault, Thomas, Mehdad, and Chang (2016); Van Hee et al. (2018).”
Challenges and frontiers in abusive content detection (2019, background): “This has been well-addressed within studies of cyberbullying, but is also highly relevant for the field of abusive content detection more widely (Van Hee et al., 2018).”