TrustMod-SM: A Multi-Axis Benchmark for Evaluating Trustworthiness of LLMs in Social Media Content Moderation

Aizan Zafar, Sreekantam Sai Venkata, Gathik Jindal, Ritu Raj Sharma, Zishan Ahmad +2

Large Language Models (LLMs) show strong potential for social media content moderation, but their trustworthiness beyond overall accuracy remains underexplored. We introduce TrustMod-SM, a benchmark of roughly 29,000 instances across eight datasets, to evaluate LLM moderators on five dimensions: trustfulness, fairness, safety, robustness, and context integrity. Evaluating 16 open-weight models (0.5B-284B parameters, spanning text-only and vision-language architectures) and one closed frontier model, we find persistent trustworthiness gaps. Higher detection accuracy often coincides with greater demographic disparity, and models frequently comply with concealment or exaggeration prompts, assign high confidence to wrong predictions, rarely escalate ambiguous cases, and struggle to separate hate speech from counter-speech or reclaimed language (especially below 8B parameters). Scaling does not uniformly resolve these issues, and no single model excels across all five dimensions.