Abstract
Detecting Cyberbullying is still an important issue. Existing approaches often rely on advanced techniques including machine learning and Natural Language Processing algorithms. In this paper, we propose an ontology and classifiers-based approach to detect cyberbullying cases in the context of social media. We propose a cyberbullying ontology in terms of cyberbullying categories and representative terms vocabulary. This ontology is used to build and annotate the toxicity of our training dataset extracted from different data sources. Various unit classifiers are used including messages toxicity detection, gender classifier, age estimation, and personality estimation. Outputs of these classifiers can be combined to intercept contents that could be cyberbullying cases.
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Notes
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Toxic comment challenges: https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data.
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Sajadi Ansari, F., Barhamgi, M., Khelifi, A., Benslimane, D. (2021). An Approach to Detect Cyberbullying on Social Media. In: Attiogbé, C., Ben Yahia, S. (eds) Model and Data Engineering. MEDI 2021. Lecture Notes in Computer Science(), vol 12732. Springer, Cham. https://doi.org/10.1007/978-3-030-78428-7_5
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