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.

翻译：社交媒体上的推荐系统日益成为用户接触心理健康内容的媒介，然而这些系统能否区分求助行为与痛苦表达仍不明确。我们通过30个全新账户及基于大语言模型（LLM）的智能体，对TikTok的“为您推荐”页面开展为期7天的受控审计，实验变量包括初始搜索框架（痛苦导向vs.求助导向）与交互策略（积极参与、回避型、被动型）。在8，727条推荐视频中，交互行为主导了曝光结果：积极参与迅速使信息流饱和心理健康内容（约占每日推荐的45%），而回避与被动浏览虽能减少却无法完全消除此类曝光（约占11-20%）。搜索框架主要改变内容构成而非数量——求助型搜索更易获得潜在支持性内容，但潜在有害内容仍以低水平非零形式存在，涵盖自杀/自残类目。研究结果表明，TikTok推荐系统对用户意图信号的敏感性有限，这促使需要针对敏感话题建立情境感知防护机制。