Abstract
Cyberbullying is becoming a major concern in online environments with troubling consequences. However, most of the technical studies have focused on the detection of cyberbullying through identifying harassing comments rather than preventing the incidents by detecting the bullies. In this work we study the automatic detection of bully users on YouTube. We compare three types of automatic detection: an expert system, supervised machine learning models, and a hybrid type combining the two. All these systems assign a score indicating the level of “bulliness” of online bullies. We demonstrate that the expert system outperforms the machine learning models. The hybrid classifier shows an even better performance.
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Dadvar, M., Trieschnigg, D., de Jong, F. (2014). Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies. In: Sokolova, M., van Beek, P. (eds) Advances in Artificial Intelligence. Canadian AI 2014. Lecture Notes in Computer Science(), vol 8436. Springer, Cham. https://doi.org/10.1007/978-3-319-06483-3_25
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DOI: https://doi.org/10.1007/978-3-319-06483-3_25
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06482-6
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