DEEP LEARNING ALGORITHMS FOR CYBERBULLING DETECTION IN SOCIAL MEDIA PLATFORMS

Abstract

Information and Communication Technologies have propelled social networking and communication, but cyber bullying poses significant challenges. Existing userdependent mechanisms for reporting and blocking cyber bullying are manual and inefficient. Conventional Machine Learning and Transfer Learning approaches were explored for automatic cyber bullying detection. The study utilized a comprehensive dataset and structured annotation process. Textual, sentiment and emotional, static and contextual word embeddings, psycholinguistics, term lists, and toxicity features were employed in the Conventional Machine Learning approach. This research introduced the use of toxicity features for cyber bullying detection. Contextual embeddings of word Convolutional Neural Network (Word CNN) demonstrated comparable performance, with embeddings chosen for its higher F-measure. Textual features, embeddings, and toxicity features set new benchmarks when fed individually. This outperformed Linear SVC in terms of training time and handling highdimensionality features. Transfer Learning utilized Word CNN for fine-tuning, achieving a faster training computation compared to the base models. Additionally, cyber bullying detection through Flask web was implemented, yielding an accuracy of 97.06%. The reference to the specific dataset name was omitted for privacy.