Despite raising concerns about the mental health effects associated with the usage of TikTok, little is known about how related content is framed by creators and received by audiences. We collect the content of 28,341 TikTok videos and 80,130 comments from Mental Health Awareness Month (May) in 2023 and 2024 via the TikTok Research API, and study how the tone of awareness varies across topics and years. We characterize "tone" as the emotional and interpersonal framing of mental health discourse, operationalized through sentiment and toxicity measures. We extract topics from video text using BERTopic and log-odds keywords, then quantify topic-conditioned sentiment (XLM-T) and toxicity (Detoxify) separately for video transcriptions and comments. Sentiment captures the affective valence of content, while toxicity reflects the presence of harmful or abusive language. We find a stable set of recurring themes across years, spanning clinical conditions, emotional disclosure, self-care, and campaign-oriented content, with engagement highly skewed toward a small subset of topics. All sentiment and toxicity analyses are computed separately for video content and comments, allowing us to distinguish between content production and audience reception. Sentiment in videos is often negative for emotionally charged topics, while comments tend to shift toward more mixed or positive polarity, especially for suicide prevention. Toxicity is low in median overall, but exhibits longer-tailed outliers in comments than in videos that are more pronounced in comments and concentrated in specific topics (e.g., "Duet", "Suicide Prevention", and "Psychisch"). Overall, our results provide a topic-level decomposition of mental health discourse on TikTok during awareness-month campaigns.

翻译：尽管对使用TikTok相关的心理健康影响存在担忧，但关于创作者如何构建相关内容以及受众如何接收这些内容，目前所知甚少。我们通过TikTok研究API收集了2023年与2024年心理健康宣传月（5月）期间28,341个TikTok视频和80,130条评论的内容，并研究了关注基调如何随话题和年份变化。我们将"基调"定义为心理健康话语的情感和人际框架，通过情感与毒性指标进行可操作化度量。我们利用BERTopic和对数几率关键词从视频文本中提取话题，然后分别量化视频转录文本和评论中基于话题的情感（XLM-T）与毒性（Detoxify）。情感反映内容的情绪效价，而毒性则体现有害或辱骂性语言的存在。我们发现跨年份存在一组稳定的重复主题，涵盖临床状况、情感披露、自我关怀和宣传导向内容，且参与度高度集中于少数话题。所有情感与毒性分析均对视频内容与评论分别计算，从而区分内容生产与受众接收。视频中情感常因情绪化话题呈现负面倾向，而评论则倾向于转向更复杂或积极的极性，尤其在自杀预防方面。毒性中位数整体较低，但评论中呈现比视频更明显的长尾异常值，且集中于特定话题（如"Duet""Suicide Prevention""Psychisch"）。总体而言，我们的结果揭示了宣传月活动期间TikTok心理健康话语的话题级分解。