Harnessing Machine Learning to Unveil Emotional Responses to Hateful Content on Social Media

Abstract

Within the dynamic realm of social media, the proliferation of harmful content can significantly influence user engagement and emotional health. This study presents an in-depth analysis that bridges diverse domains, from examining the aftereffects of personal online attacks to the intricacies of online trolling. By leveraging an AI-driven framework, we systematically implemented high-precision attack detection, psycholinguistic feature extraction, and sentiment analysis algorithms, each tailored to the unique linguistic contexts found within user-generated content on platforms like Reddit. Our dataset, which spans a comprehensive spectrum of social media interactions, underwent rigorous analysis employing classical statistical methods, Bayesian estimation, and model-theoretic analysis. This multi-pronged methodological approach allowed us to chart the complex emotional responses of users subjected to online negativity, covering a spectrum from harassment and cyberbullying to subtle forms of trolling. Empirical results from our study reveal a clear dose–response effect; personal attacks are quantifiably linked to declines in user activity, with our data indicating a 5% reduction after 1–2 attacks, 15% after 3–5 attacks, and 25% after 6–10 attacks, demonstrating the significant deterring effect of such negative encounters. Moreover, sentiment analysis unveiled the intricate emotional reactions users have to these interactions, further emphasizing the potential for AI-driven methodologies to promote more inclusive and supportive digital communities. This research underscores the critical need for interdisciplinary approaches in understanding social media’s complex dynamics and sheds light on significant insights relevant to the development of regulation policies, the formation of community guidelines, and the creation of AI tools tailored to detect and counteract harmful content. The goal is to mitigate the impact of such content on user emotions and ensure the healthy engagement of users in online spaces.