Exploring the impact of social network structures on toxicity in online mental health communities

Abstract

This study examines how structural social capital influences online toxicity within mental health communities. Using social network analysis and regression models, we analyze both direct and interaction effects of network centralities—degree, closeness, eigenvector, and betweenness—on toxicity in the r/MentalHealth subreddit. From a dataset of 90,626 posts, we constructed a network of 7562 users interconnected through 12,699,868 relationships. Our findings highlight the nuanced relationship between network positioning and toxic behavior. Users with a higher degree centrality, reflecting broad connectivity, exhibit lower toxicity levels, indicating that well-connected individuals contribute positively to community dynamics. Conversely, higher eigenvector, closeness, and betweenness centralities are associated with increased toxicity, suggesting that influential users, those centrally located, and those acting as bridges between network segments are more likely to engage in toxic behavior. Interaction effects further reveal complexities: for instance, well-connected and influential users tend to mitigate toxicity, while those who combine influence with proximity amplify it. These insights underscore the dual role of network structures in moderating or exacerbating harmful interactions. The study offers actionable strategies for fostering healthier online environments by leveraging network centralities to design targeted interventions and reduce toxicity in online mental health communities.