Abstract
Abstract: Cyberbullying has emerged as a significant issue in today's social media landscape, posing various detrimental effects. The combination of photo sharing and text comments has exacerbated the severity of cyberbullying incidents. Automated detection tools are crucial for ensuring the health and security of these platforms. However, traditional approaches that analyze text and images separately may fail toidentify all instances of cyberbullying, especially whenseemingly innocent content conveys bullying messages when posted together. This research proposes a novel system that extracts combined features from text and images to identify diverse cases of cyberbullying. The system can extract profiles based on behavior and uncover latent ties between users and groups with similar behaviors. Our approach utilizes methods log mining, business analysis, complex networks, and graph theory to achieve this. This paper outlines the entire process, from log file analysis to the construction of the user graph, witha particular focus on the step known as The finding of user behavioral patterns.
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