Seeking Help, Facing Harm: Auditing TikTok’s Mental Health Recommendations - Jamie, Pooriya; Ghasemian, Amir; Hosseinmardi, Homa (Proceedings of the International AAAI Conference on Web and Social Media, 2026)
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.