Shielding Online Communities: Natural Language Processing and Machine Learning Strategies against Social Media Intimidation

Abstract

With the usage of the internet and the growing prominence of communities, like social media we have witnessed a rise in cybercrime. Among these crimes one that stands out is Intimidator, which affects both people and adults alike. The increasing incidents of cyberbullying have led to consequences such as anxiety, aggression, depression and tragically even suicide. Consequently, there is now a pressing need for content regulation on social media platforms. This research focuses on developing a model of identifying text-based bullying messages and comments by categorizing them into five distinct types; Violence, Vulgar language Offensive content, sexually explicit material, and Hate Speech. The proposed approach involves utilizing Natural Language Processing (NLP) techniques with Machine Learning methods. The dataset is initially. Processed to remove information before extracting meaningful features. Finally, the model undergoes training and testing to ensure reliable results, in detecting instances of Intimidator in text-based data.