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Combining textual features to detect cyberbullying in social media posts

Meisy Fortunatus, Patricia Anthony, Stuart Charters

2020Procedia Computer Science33 citationsDOIOpen Access PDF

Abstract

Cyberbullying has become prevalent in social media communication. To create a safe space for cyber communication, an effective cyberbullying detection method is needed. This study focuses on using combination of textual features to detect cyberbullying across social media platforms. Lexicon enhanced rule-based method was applied to detect cyberbullying on Facebook comments. The resulting algorithm was evaluated using performance measures of accuracy, precision, recall, and F1 Score, and showed promising performance with average recall of 95.981%.

Topics & Concepts

Computer scienceSocial mediaLexiconRecallMicrobloggingPrecision and recallSpace (punctuation)Artificial intelligenceWorld Wide WebPsychologyOperating systemCognitive psychologyHate Speech and Cyberbullying DetectionBullying, Victimization, and AggressionStalking, Cyberstalking, and Harassment
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