Abstract
Monitoring opioid-related chatter on social media can predict the course of opioid addiction and the overdose epidemic. We assessed the utility of TikTok, a prominent short video-based social media platform, as a means of tracking the opioid addiction and overdose crisis. We collected 569,581 TikTok comments (posted between January 2021 and June 2025) from 48,306 opioid-related videos, making this study the first large-scale analysis of TikTok comments for opioid surveillance. We extracted 200 topics from these comments using Latent Dirichlet Allocation (LDA) and incorporated the topics into ARIMA models that forecast synthetic opioid mortality over 6-month horizons. We also analyzed conversational patterns using the LIWC2015 pronoun dictionaries and GPT o1-mini. We found that (1) incorporating TikTok topics into the ARIMA models reduced forecasting Mean Absolute Error by up to 37% (2) the topics spanned five broad themes (use, source, recovery, harm-reduction, loss), showing the diversity of opioid discourse on TikTok, and (3) TikTok comments included first-person, second-person, and third-person accounts of opioid use (i.e., personal use, engaging with other users in conversation about their use, and relating views of others’ use, respectively). These findings emphasize the usefulness of TikTok comments as a data source for opioid use surveillance.
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Acknowledgements
The authors would like to thank Mathew Kiang, Samuel Campione, Prakhar Goel, Cedric (Chun Hui) Lim, and members of the Altman Lab for insightful conversations and assistance. We would also like to thank the Microsoft Accelerate Foundation Models Research Program for Azure GPT credits. This work is supported by NIH DA057598 and NIH DA057605; IAS is supported by the Stanford Bioengineering Department and Sarafan ChEM-H CBI Program Award; K.A.C. is supported by NIH 1F31GM151783. D.A.S. is supported by the Stanford Biochemistry Department and NSF GRFP 2019286895. K.H. is supported by a Senior Research Career Scientist Award (RCS 04-141-3) from the Department of Veterans Affairs Health System Research Service. J.C.E., R.B.A. and A.L. are supported by the Stanford Institute for Human-Centered AI. R.B.A. is supported by the Chan Zuckerberg Biohub and NIH GM153195.
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Samori, I.A., Carpenter, K.A., Smith, D.A. et al. TikTok is a valuable data source for tracking the opioid crisis. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02654-x
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DOI: https://doi.org/10.1038/s41746-026-02654-x
