When Large Language Models and Machine Learning Meet Multi-Criteria Decision Making: Fully Integrated Approach for Social Media Moderation

This study demonstrates a novel integration of large language models, machine learning, and multi-criteria decision-making to investigate self-moderation in small online communities, a topic under-explored compared to user behavior and platform-driven moderation on social media. The proposed methodological framework (1) utilizes large language models for social media post analysis and categorization, (2) employs k-means clustering for content characterization, and (3) incorporates the TODIM (Tomada de Decisão Interativa Multicritério) method to determine moderation strategies based on expert judgments. In general, the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems. When applied in social media moderation, this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location. The application of this framework is demonstrated within Facebook groups. Eight distinct content clusters encompassing safety, harassment, diversity, and misinformation are identified. Analysis revealed a preference for content removal across all clusters, suggesting a cautious approach towards potentially harmful content. However, the framework also highlights the use of other moderation actions, like account suspension, depending on the content category. These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.