TORA: Topic-based rule affinity for content moderation in social media communities

While social media platforms enable the exchange of information and experiences across online communities, their extensive connectivity simultaneously accelerates the dissemination of misleading and potentially harmful content. Content moderation serves as a crucial mechanism for curbing such dissemination, with community rules playing a central role in guiding moderation decisions. Despite significant advances in automated content moderation, the explicit incorporation of community rules into model design remains rare. Among the different levels of social aggregation where community rules operate, macro norms establish standardized governance principles to ensure consistency across communities; however, they remain largely overlooked in automated content moderation research. To address these gaps, we propose TOpic-based Rule Affinity (TORA), a content moderation framework that draws on and adapts polycentric governance theory, algorithmic governance theory, and sociotechnical systems theory to incorporate rules from heterogeneous communities into moderation. Following the design science paradigm, TORA introduces two novel design artifacts: topic-based macro norms, which use Large Language Models (LLMs) to capture and standardize rules across diverse communities, and a topic-based rule affinity measure, which quantifies the relationship between user-generated content and those norms. We implement and evaluate TORA using data collected from multiple social media communities. Empirical results show that the framework consistently outperforms state-of-the-art baseline models, and each of its two key design artifacts contributes significantly to improved performance. These findings make significant theoretical and design contributions and have implications for improving societal outcomes by enabling more interpretable and transparent content moderation and supporting fairness and justice in online governance.