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

arXiv: https://arxiv.org/abs/2606.13581 · HTML: https://mlsanity.com/paper/2606.13581/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 11 Jun 2026.

## Builds on (most cited first, 11 of 11)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond](https://mlsanity.com/paper/2104.12250/) (2021)
- [Studying Behavioral Addiction by Combining Surveys and Digital Traces: A Case Study of TikTok](https://mlsanity.com/paper/2501.15539/) (2025)
- [What we can learn from TikTok through its Research API](https://mlsanity.com/paper/2402.13855/) (2024)
- [Experiences of Censorship on TikTok Across Marginalised Identities](https://mlsanity.com/paper/2407.14164/) (2024)
- [Just Another Hour on TikTok: ID sampling to obtain a complete slice of TikTok](https://mlsanity.com/paper/2504.13279/) (2025)
- [Towards an Automated Framework to Audit Youth Safety on TikTok](https://mlsanity.com/paper/2509.05838/) (2025)
- [TikTok's Research API: Problems Without Explanations](https://mlsanity.com/paper/2506.09746/) (2025)
- [Interpreting Multimodal Communication at Scale in Short-Form Video: Visual, Audio, and Textual Mental Health Discourse on TikTok](https://mlsanity.com/paper/2601.15278/) (2026)

## Abstract (arXiv)

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.