Evidence map›Paper›PMID 41232106›Full record

Observational studyJournal of medical Internet research2025

Identifying Stigma Phenotypes in Social Media Narratives of Substance Use: Observational Study.

Lexie Chenyue Wang, Kenneth C Pike, Mike Conway, Annie T Chen

Abstract readObservational Study
In one paragraph

Observational study in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Lexie Chenyue WangDepartment of Linguistics, University of Washington, Seattle, WA, United States.ORCID https://orcid.org/0009-0002-9463-0769
Kenneth C PikeOffice of Nursing Research, University of Washington, Seattle, WA, United States.ORCID https://orcid.org/0000-0002-7730-6476
Mike ConwaySchool of Computing and Information Systems, University of Melbourne, Melbourne, Australia.ORCID https://orcid.org/0000-0002-3209-8108
Annie T ChenDepartment of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, WA, United States.ORCID https://orcid.org/0000-0003-3070-8336

Funding

Using Narratives to Identify Stigma Phenotypes - A Socio-Ecological ApproachR21DA056684 · NIDA · UNIVERSITY OF WASHINGTON · PI CHEN, ANNIE · 2022 to 2023
$489k
NIDA NIH HHS R21 DA056684
6 · The paper itself

Abstract

backgroundIndividuals with substance use problems experience stigma in different contexts. Identifying characteristic situations in which stigma occurs or manifests-stigma phenotypes-can serve as important leverage points for future intervention.

objectiveThis paper aims to (1) identify stigma phenotypes expressed in social media narratives related to substance use stigma and (2) explore the similarities and differences between the stigma phenotypes from a social ecological perspective.

methodsWe collected Reddit posts pertaining to 3 substances-alcohol, cannabis, and opioids. We performed feature engineering using a combination of content analysis, machine learning, and keyword-based methods to predict variables at different levels of the social ecological framework. Leveraging these features, we applied the fuzzy c-means clustering algorithm on the subset of posts containing stigma to extract stigma phenotypes, where a phenotype is defined by four main dimensions: (1) the stigma mechanism present (eg, internalized stigma, anticipated stigma, or enacted stigma), (2) the substance used (eg, alcohol, cannabis, or opioids), (3) the settings involved (eg, work, school, or home), and (4) the actors involved (eg, family, friends, or partners). Finally, we used Kruskal-Wallis and Dunn post hoc tests to examine the differences between stigma phenotypes with respect to specific ecological factors.

resultsWe derived 7 stigma phenotypes from stigma-related posts by 8627 authors. The phenotypes can be categorized into 4 groups: internalized stigma-only, anticipated stigma, enacted stigma-only, and mixed-stigma phenotypes. Narratives on internalized stigma phenotypes focused on the self, with minimal reference to settings and actors. One phenotype focused on anticipated stigma and was characterized by a high proportion of opioid use mentions (707/1217, 58.09% of the authors) and references to the health care setting (647/1217, 53.16% of the authors). Posts associated with the enacted stigma-only phenotypes included substantial representation of settings and actors. Narratives in the mixed-stigma phenotypes often involved more than one stigma mechanism, setting, and actor, with home and family being the most salient factors. The phenotypes differed from one another with respect to social ecological factors, including loneliness and social isolation, use of treatment services, presence of health care providers, community and support groups, society, and legalization.

conclusionsThese findings provide valuable insights that help inform the design and development of interventions targeted at different stigma phenotypic groups from a social ecological perspective.

Indexed as

Social MediaSocial StigmaSubstance-Related DisordersHumansNarrationPhenotypemachine learningsocial ecologicalsocial mediastigmasubstance use

Identifiers

PMID41232106
PMCPMC12661227

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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.