Combining textual features to detect cyberbullying in social media posts
Meisy Fortunatus, Patricia Anthony, Stuart Charters
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