Cyberbullying Detection and Prevention System for Enhancing Online Platform Safety Using Maximum Entropy Model

ABSTRACT The widespread proliferation of social media platforms in the 21st century has undoubtedly brought about a significant shift in how individuals establish connections. However, it is essential to acknowledge that this technological innovation has also given rise to negative repercussions. The proliferation of online abuse, harassment, cyberbullying, and trolling has emerged as a significant concern, overshadowing the otherwise advantageous aspects of digital networking platforms. The detrimental effect of cyberbullying is of considerable concern, which often causes profound psychological and physiological anguish among its targets, sometimes resulting in contemplation of suicide. The proliferation of online harassment occurrences, including disclosing confidential dialogues and spreading detrimental rumors, has engendered extensive apprehension. The present research aims to address the pressing need to identify and mitigate cyberbullying within the context of social media platforms. A rigorous approach was used to gather data from Kaggle, which was further processed utilizing sophisticated methods like tokenization, stemming, and lemmatization. The research used the Maximum Entropy Model to develop a detection system to identify instances of bullying in texts or messages shared on social media sites. The algorithm exhibited a praiseworthy mean accuracy rate of 77% in detecting cases of cyberbullying. The results highlight the capacity of the algorithm to anticipate if remarks entered by individuals on social media might be classified as cyberbullying, representing a noteworthy advancement in addressing this widespread problem. This study supports the broader deployment of the created cyberbullying detection system across all social media platforms. The proactive use of this novel technology enables online platforms to effectively detect and mitigate cyberbullying, cultivating a digital environment characterized by enhanced safety and respect. This research illuminates the significant issue of cyberbullying. It presents a concrete remedy to protect the welfare of those who engage in online activities, particularly those more susceptible to harm, such as women and children.