Monitoring the opioid epidemic via social media discussions

  1. Delaney A. Smith
  2. Adam Lavertu
  3. Aadesh Salecha
  4. Tymor Hamamsy
  5. Keith Humphreys
  6. Anna Lembke
  7. Mathew V. Kiang
  8. Russ B. Altman
  9. Johannes C. Eichstaedt

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Abstract

The opioid epidemic persists in the U.S., with over 80,000 deaths annually since 2021, primarily driven by synthetic opioids. Responding to this evolving epidemic requires reliable and timely information. One source of data is social media platforms. We assessed the utility of Reddit data for surveillance, covering heroin, prescription, and synthetic drugs. We built a natural language processing pipeline to identify opioid-related content and created a cohort of 1,689,039 Reddit users, each assigned to a state based on their previous Reddit activity. We measured their opioid-related posts over time and compared rates against CDC overdose and NFLIS report rates. To simulate the real-world prediction of synthetic opioid overdose rates, we added near real-time Reddit data to a model relying on CDC mortality data with a typical 6-month reporting lag. Reddit data significantly improved the prediction accuracy of overdose rates. This work suggests that social media can help monitor drug epidemics.

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  1. Version published to 10.1038/s41746-025-01642-x
  2. ScreenIT

    SciScore for 10.1101/2021.04.01.21254815: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    A key limitation of this study is that we do not distinguish between drug mentions and probable drug use, as our calculation of drug comment rates uses the number of mentions of a drug, not the number of drug mentions indicative of drug use. Future methods could make use of methods that have been developed to differentiate abuse from discussions, potentially improving accuracy of the overall system33. It is also possible that since we are not selecting for only comments regarding drug usage, that the rates of drug discussions we observe are driven by news cycles. While there is certainly an interplay between the news coverage and online discussions of drugs, the prevalence of kratom discussion on Reddit indicates that discourse can evolve without mainstream news coverage, as kratom is a drug with very low media coverage but high rates of discussion. Additionally, during the COVID-19 pandemic of the past year, changes in opioid overdose rates have received relatively little press coverage. Therefore, we view it as unlikely that news coverage drove the observed recent changes in opioid discussions or the emergence of kratom related activity. There is potential for interplay and response from the drug abuse community to the existence of a future social media surveillance system. If the drug abuse community sought to avoid surveillance efforts, those communities could find or create new platforms in which to converse, which could potentially facilitate private discussions. Indeed...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.04.01.21254815 on medRxiv