Recommender systems on social media increasingly mediate how users encounter mental health content, yet it remains unclear whether they distinguish help-seeking from distress expression. We conduct a controlled 7-day audit of TikTok's "For You" page using 30 fresh accounts and LLM-guided agents that vary initial search framing (distress- vs. help-initiated) and interaction strategy (engaged, avoidant, passive). Across 8,727 recommended videos, interaction behavior dominates exposure outcomes: engagement rapidly saturates feeds with mental health content (~45% of daily recommendations), while avoidance and passive viewing reduce but do not eliminate exposure (~11-20%). Search framing mainly shifts composition rather than volume--help-initiated searches yield more potentially supportive material, yet potentially harmful content persists at low but non-zero levels, including content in the Suicide/Self-Harm category. These findings suggest limited sensitivity to user intent signals in TikTok's recommendations and motivate context-aware safeguards for sensitive topics.
@article{arxiv.2604.14832,
title = {Seeking Help, Facing Harm: Auditing TikTok's Mental Health Recommendations},
author = {Pooriya Jamie and Amir Ghasemian and Homa Hosseinmardi},
journal= {arXiv preprint arXiv:2604.14832},
year = {2026}
}
Comments
Accepted at the Proceedings of the International AAAI Conference on Web and Social Media (ICWSM 2026)