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Record W4400745720 · doi:10.2196/59193

Mpox Discourse on Twitter by Sexual Minority Men and Gender-Diverse Individuals: Infodemiological Study Using BERTopic

2024· article· en· W4400745720 on OpenAlexvenueno aff
Yunwen Wang, Karen O’Connor, Ivan Flores, Carl T. Berdahl, Ryan J. Urbanowicz, Robin Stevens, José A. Bauermeister, Graciela Gonzalez‐Hernandez

Bibliographic record

VenueJMIR Public Health and Surveillance · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPoxvirus research and outbreaks
Canadian institutionsnot available
FundersU.S. National Library of Medicine
KeywordsPreprintPsychologySocial psychologyGender studiesSociologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: The mpox outbreak resulted in 32,063 cases and 58 deaths in the United States and 95,912 cases worldwide from May 2022 to March 2024 according to the US Centers for Disease Control and Prevention (CDC). Like other disease outbreaks (eg, HIV) with perceived community associations, mpox can create the risk of stigma, exacerbate homophobia, and potentially hinder health care access and social equity. However, the existing literature on mpox has limited representation of the perspective of sexual minority men and gender-diverse (SMMGD) individuals. OBJECTIVE: To fill this gap, this study aimed to synthesize themes of discussions among SMMGD individuals and listen to SMMGD voices for identifying problems in current public health communication surrounding mpox to improve inclusivity, equity, and justice. METHODS: We analyzed mpox-related posts (N=8688) posted between October 2020 and September 2022 by 2326 users who self-identified on Twitter/X as SMMGD and were geolocated in the United States. We applied BERTopic (a topic-modeling technique) on the tweets, validated the machine-generated topics through human labeling and annotations, and conducted content analysis of the tweets in each topic. Geographic analysis was performed on the size of the most prominent topic across US states in relation to the University of California, Los Angeles (UCLA) lesbian, gay, and bisexual (LGB) social climate index. RESULTS: BERTopic identified 11 topics, which annotators labeled as mpox health activism (n=2590, 29.81%), mpox vaccination (n=2242, 25.81%), and adverse events (n=85, 0.98%); sarcasm, jokes, and emotional expressions (n=1220, 14.04%); COVID-19 and mpox (n=636, 7.32%); government or public health response (n=532, 6.12%); mpox symptoms (n=238, 2.74%); case reports (n=192, 2.21%); puns on the naming of the virus (ie, mpox; n=75, 0.86%); media publicity (n=59, 0.68%); and mpox in children (n=58, 0.67%). Spearman rank correlation indicated significant negative correlation (ρ=-0.322, P=.03) between the topic size of health activism and the UCLA LGB social climate index at the US state level. CONCLUSIONS: Discussions among SMMGD individuals on mpox encompass both utilitarian (eg, vaccine access, case reports, and mpox symptoms) and emotionally charged (ie, promoting awareness, advocating against homophobia, misinformation/disinformation, and health stigma) themes. Mpox health activism is more prevalent in US states with lower LGB social acceptance, suggesting a resilient communicative pattern among SMMGD individuals in the face of public health oppression. Our method for social listening could facilitate future public health efforts, providing a cost-effective way to capture the perspective of impacted populations. This study illuminates SMMGD engagement with the mpox discourse, underscoring the need for more inclusive public health programming. Findings also highlight the social impact of mpox: health stigma. Our findings could inform interventions to optimize the delivery of informational and tangible health resources leveraging computational mixed-method analyses (eg, BERTopic) and big data.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.288
Threshold uncertainty score0.653

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.119
GPT teacher head0.396
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2024
Admission routes1
Has abstractyes

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