TikTok is a valuable data source for tracking the opioid crisis.

Issah A Samori, Kristy A Carpenter, Delaney A Smith, Keith Humphreys, Anna Lembke, Johannes C Eichstaedt, Russ B Altman

Abstract read

In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Issah A SamoriDepartment of Bioengineering, Stanford University, Stanford, CA, USA. iasamori@stanford.edu.

Kristy A CarpenterDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA.

Delaney A SmithDepartment of Biochemistry, Stanford University, Stanford, CA, USA.

Keith HumphreysDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.

Anna LembkeDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.

Johannes C Eichstaedt *Department of Psychology, Stanford University, Stanford, CA, USA.

Russ B Altman *Department of Bioengineering, Stanford University, Stanford, CA, USA. rbaltman@stanford.edu.

Funding

National Science Foundation NSF GRFP 2019286895NIH HHS NIH 1F31GM151783NIH HHS NIH DA057598; NIH DA057605NIH HHS RCS 04-141-3

6 · The paper itself

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.

Identifiers

PMID42045414

DOI10.1038/s41746-026-02654-x

PMCPMC13324712

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.