Integrating Link Prediction and Comment Analysis for Enhanced Cyberbullying Detection in Online Social Interactions

With the widespread use of online platforms and social media, live chatting has become an integral part of our digital interactions. Individuals are dedicating excessive time to social media applications, facilitated by the development of the internet, technology, and Social Network services like Facebook, Twitter, Instagram, and WhatsApp. While some users utilize these platforms for relaxation and spreading positivity, others misuse them by disseminating hateful content, leading to negative emotional impacts such as anger, stress, depression, and even suicide. Cyberbullying, characterized by the transmission of threatening or insulting messages, exacerbates this issue. Users frequently engage in unproductive debates, resulting in mutual insults and wasted time. Although various enhanced cyberbullying detection techniques have been developed, their effectiveness, especially in live chat scenarios, remains a topic of interest. This paper proposes a novel approach utilizing similarity scores as social network features to predict the probability of links between individuals engaged in live chat, enabling the application of restrictions on hate speech transmission and also optimization in friend recommendation systems. Additionally, it explores the classification of posts from popular social media pages into hateful and non-hateful categories. By analyzing comments and replies to these posts, insights into public behavior and reactions can be gained, facilitating the implementation of warnings or restrictions for page administrators.