Abstract: This project intends to develop a comprehensive categorization system based on machine learning to address the complex concerns of online harassment and discrimination on social media platforms. Inspired by recent research advocating for the use of machine learning in social media moderation, this project builds on existing methodologies to create a comprehensive framework capable of identifying various types of harmful content, such as cyber and non-cyber bullying, as well as discrimination based on ethnicity, gender, age, and religion. Machine learning models such as Naive Bayes, SVM, Random Forest, Decision Tree, and Sklearn classifiers are trained to detect patterns and subtle nuances indicative of online abuse and discrimination by utilizing diverse datasets representing instances of harmful behavior across multiple dimensions. The suggested categorization system's performance and flexibility are tested by comprehensive testing and assessment on real-world social media data. The technology provides timely and precise identification of hazardous information by combining different categorization tasks under a uniform framework, allowing social media platforms to handle its propagation proactively. Furthermore, the use of machine learning algorithms improves the scalability and effectiveness of content moderation activities, reducing the burden on human moderators and creating a safer and more inclusive online environment. This study contributes to a better understanding of the complex dynamics of online abuse and discrimination, enabling the creation of nuanced solutions for enhancing online safety and content control.
Keywords: child predators, cyber harassers, Twitter, machine learning.
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DOI:
10.17148/IJARCCE.2025.14405
