分析抖音心理健康月内容情绪与毒性，揭示创作者与观众的差异反应。
The Tone of Awareness: Topic, Sentiment, and Toxicity Maps During Mental Health Month on TikTok
- 用BERTopic提取话题，分年对比视频与评论的情绪和毒性。
- 视频情绪多负面，评论则更积极，尤其在自杀预防话题中。
- 评论毒性虽低但存在长尾异常，集中在特定话题如‘Duets’。
我们通过TikTok研究API收集了2023年和2024年心理健康意识月（5月）期间共28,341条视频及80,130条评论，研究内容基调在不同话题和年份间的差异。将‘基调’定义为心理健康的语义与人际表达方式，通过情感（XLM-T）与毒性（Detoxify）指标量化。利用BERTopic和对数似然关键词提取视频文本中的主题，分别计算视频与评论的情感和毒性。发现跨年有稳定重复的主题，包括临床病症、情绪表达、自我关怀与活动导向内容，且互动高度集中于少数话题。视频情绪在情感强烈话题中多为负面，而评论则趋向混合或正面，尤以自杀预防话题明显。整体毒性水平较低，但评论中存在更显著的长尾异常，集中在特定话题如‘Duet’、‘Suicide Prevention’和‘Psychisch’。结果提供了心理健康议题在平台传播中的主题级解析。
原文摘要 · Abstract (English)
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.
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