The Tone of Awareness: Topic, Sentiment, and Toxicity Maps During Mental Health Month on TikTok

2026-06-11

AI Summary

This study addresses a critical gap in understanding TikTok mental health content by systematically examining its creation and reception, with particular attention to emotional valence, topic distribution, and harmful discourse. Analyzing video and comment data from Mental Health Awareness Months in 2023 and 2024, the research presents the first joint analysis of sentiment polarity and toxicity at the topic level. Leveraging BERTopic for thematic modeling, XLM-T for multilingual sentiment analysis, Detoxify for toxicity detection, and log-odds ratio for keyword identification, the findings reveal a stable cross-year topic structure in mental health discussions. Videos exhibit predominantly negative sentiment, whereas comments are generally more positive. Although overall toxicity remains low, a significant long-tailed toxicity anomaly emerges in comments associated with high-risk topics such as suicide prevention.

Abstract

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.

Problem

• mental health
• sentiment
• toxicity
• TikTok
• topic modeling

Innovation

• tone of awareness
• topic-conditioned sentiment
• toxicity analysis
• BERTopic
• content-reception gap