Evidence map›Paper›PMID 40293990›Full record

ArticlePLOS digital health2025

Which social media platforms facilitate monitoring the opioid crisis?

Kristy A Carpenter, Anna T Nguyen, Delaney A Smith, Issah A Samori, Keith Humphreys, Anna Lembke, Mathew V Kiang, Johannes C Eichstaedt, Russ B Altman

Abstract read
In one paragraph

Article in PLOS digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Kristy A CarpenterDepartment of Biomedical Data Science, Stanford University, Stanford, California, United States of America.ORCID https://orcid.org/0000-0003-4570-5170
Anna T NguyenDepartment of Epidemiology and Population Health, Stanford University, Stanford, California, United States of America.
Delaney A SmithDepartment of Biochemistry, Stanford University, Stanford, California, United States of America.
Issah A SamoriDepartment of Bioengineering, Stanford University, Stanford, California, United States of America.
Keith HumphreysDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, United States of America.
Anna LembkeDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, United States of America.
Mathew V KiangDepartment of Epidemiology and Population Health, Stanford University, Stanford, California, United States of America.
Johannes C EichstaedtDepartment of Psychology, Stanford University, Stanford, California, United States of America.
Russ B AltmanDepartment of Biomedical Data Science, Stanford University, Stanford, California, United States of America.

Funding

Stanford BSSR Pre-Doctoral Training Program at the Intersection of Data Sciences with Behavioral, Social, and Population Health ResearchT32HL151323 · NHLBI · STANFORD UNIVERSITY · PI Michelle Christina Odden, David H Rehkopf · 2020 to 2026
$1.6M
Computational methods for characterizing sources of variability in drug responseR35GM153195 · NIGMS · STANFORD UNIVERSITY · PI RUSS BIAGIO ALTMAN · 2024 to 2026
$1.0M
Tracking the opioid epidemic with social media: an early warning systemR21DA057598 · NIDA · STANFORD UNIVERSITY · PI ALTMAN, RUSS BIAGIO · 2022 to 2022
$433k
NHLBI NIH HHS T32 HL151323NIDA NIH HHS R21 DA057598NIGMS NIH HHS R35 GM153195
6 · The paper itself

Abstract

Social media can provide real-time insight into trends in substance use, addiction, and recovery. Prior studies have used platforms such as Reddit and X (formerly Twitter), but evolving policies around data access have threatened these platforms' usability in research. We evaluate the potential of a broad set of platforms to detect emerging trends in the opioid use disorder and overdose epidemic. From these, we identified 11 high-potential platforms, for which we documented policies regulating drug-related discussion, data accessibility, geolocatability, and prior use in opioid-related studies. We quantified their volume of opioid discussion, including in informal language by including slang generated using a large language model. Beyond the most commonly used Reddit and X/Twitter, the platforms with high potential for use in opioid-related surveillance are TikTok, YouTube, and Facebook. Leveraging a variety of social platforms, instead of merely one, yields broader subpopulation representation and safeguards against reduced data access in any single platform.

Identifiers

PMID40293990
PMCPMC12036940

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.