Analyzing the Dynamics of Hate Speech on Online Platforms

In the rapidly evolving landscape of online microblogging platforms, hate speech has emerged as a particularly troubling issue. Alarmingly, numerous countries have seen a sharp increase in hate crimes driven by malicious hate campaigns. While the detection of hate speech has gained attention as a major research field, the complexities of its genesis and spread across online social networks remain largely unexamined. In this study, we focus on developing a benchmark dataset for hate and abusive speech, related to various aspects of Indian politics, and religious topics. The dataset comprises of a vast user base of politically active individuals, enabling us to capture the nuances of hate speech propagation within this context. Additionally, we employ advanced topic modeling techniques to analyze the data, uncovering the underlying themes and user reactions associated with the data. This detailed analysis reveals how people respond to these topics, the temporal activation of discussions, and the comparative trends between different hashtags. This study offers valuable insights for policymakers, social media companies, and researchers, helping them develop better strategies to reduce hate speech and promote positive online interactions.