Influence Ranking of Hate Users in Content Propagation on Twitter

Social media platforms are now essential for communication, but they also lead to the quick spread of hate speech, which leaves people and society at serious risk. To lessen the influence of hate content, it is crucial to identify the top influential users who spread it. This research ranks the influence of hate users who propagate Twitter content. A novel method of Influence Score for measuring user influence was developed by integrating activity and engagement metrics, such as the number of hate tweets, retweets, likes, and replies. Users were divided into LOW, MEDIUM, and HIGH influence groups using k-means clustering, which allowed for a more focused examination of user behaviour. The findings showed that a smaller percentage of HIGH-influence users contribute to spreading hate content than users with LOW and MEDIUM influence. The validation process made clear how much better the suggested influence score was than followers, highlighting the value of using many metrics to find real influencers. The validation of the Influence Score was confirmed by comparison with crowdsourced rankings. With its scalable approach for detecting and classifying the influence of hate users, this study gives policymakers and social media administrators useful information. Platforms can significantly reduce the spread of hate content by targeting user clusters based on their influence levels and implementing actions based on the severity of each cluster. Future researchers can adopt this as a validation method for evaluating and improving their hate user ranking approaches.