A COMPREHENSIVE REVIEW ON CYBERBULLYING PREVENTION

Abstract

The rise of cyberbullying in the digital age necessitates innovative approaches for prevention and intervention. This project proposes a probabilistic framework for Cyber Bullying Prevention utilizing machine learning techniques, support Vector Machines (SVM) and Naïve Bayes classifiers, in particular, are the focus.. The objective is to develop a robust system capable of identifying and mitigating instances of cyberbullying in online environments. The proposed model integrates advanced natural language processing and sentiment analysis to effectively analyze textual content and contextual cues within digital communications. By employing SVM and Naïve Bayes algorithms, the system aims to discern patterns indicative of cyberbullying behavior, achieving a probabilistic assessment of the likelihood of such occurrences. Through a training dataset enriched with diverse cyberbullying instances, the model learns to generalize its understanding and adapt to evolving online communication dynamics. The project further explores feature engineering and optimization techniques to enhance the classifiers performance. The ultimate goal is to provide a proactive and accurate cyberbullying prevention tool that empowers users, platform administrators, and law enforcement agencies to intervene and mitigate the impact of cyberbullying effectively. This research contributes to the broader discourse on utilizing machine learning in social contexts, emphasizing the importance of collaborative efforts to create a safer digital space for all users.