Evidence map›Paper›PMID 42335474›Full record

ArticleJournal of medical Internet research2026

Reddit Discussions During the 2022 Mpox Outbreak: Observational Analysis of Sentiment, Topics, and Audience Engagement.

Xi Ning Luo, Zahra Movahedi Nia, Jude Dzevela Kong

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Xi Ning LuoArtificial Intelligence and Mathematical Modeling Lab, Dalla Lana School of Public Health, University of Toronto, 155 College St, Room 500, Toronto, ON, M5T 3M7, Canada, 1 416 978 0901.ORCID http://orcid.org/0009-0008-5954-7525
Zahra Movahedi NiaArtificial Intelligence and Mathematical Modeling Lab, Dalla Lana School of Public Health, University of Toronto, 155 College St, Room 500, Toronto, ON, M5T 3M7, Canada, 1 416 978 0901.ORCID http://orcid.org/0000-0002-5528-638X
Jude Dzevela KongArtificial Intelligence and Mathematical Modeling Lab, Dalla Lana School of Public Health, University of Toronto, 155 College St, Room 500, Toronto, ON, M5T 3M7, Canada, 1 416 978 0901.ORCID http://orcid.org/0000-0002-7557-5672

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Public health crises often reshape online discourse by amplifying uncertainty, frustration, stigma, and misinformation, with important implications for risk communication. Objective: This study examines these dynamics on Reddit (Reddit Inc) during a recent outbreak, using Mpox as a case study. Methods: We analyzed sentiment, topical themes, and audience engagement in posts and comments drawn from 4 Mpox-related subreddits. Using natural language processing methods, we applied sentiment analysis and latent Dirichlet allocation to classify 1169 posts and 6571 comments (from July 21, 2021, to July 16, 2025) into sentiment categories and 9 distinct topics. Of the 1169 posts, 611 (52.3%) were neutral, 370 (31.6%) were negative, and 188 (16.1%) were positive. Among comments, 2825 of 6571 (43%) were neutral, 1962 (29.9%) were negative, and 1784 (27.1%) were positive. We then used Kruskal-Wallis tests, Dunn post hoc comparisons, and Vargha-Delaney A to assess relationships among sentiment, topic, and engagement metrics. Results: Engagement differed significantly by sentiment (P<.001) and topic (P<.001). Negative posts had higher median scores (median 7, IQR 2-27) than positive ones (median 5, IQR 2-16; z score=6.02; adjusted P<.001; Vargha-Delaney A=0.55). Posts about systemic public health failures (Topic 4) received lower median scores (median 4, IQR 1.75-14.25) than other topics. Topic 9 accounted for 980 of 6571 (14.9%) comments, dominating discussions regardless of original post topic. Positive posts generated 284 of 922 (30.8%) positive comments, whereas negative posts received 526 of 1615 (32.6%) negative comments. Comments on positive posts had higher sentiment scores (Vargha-Delaney A=0.550), whereas comments on negative posts had lower sentiment scores (Vargha-Delaney A=0.463). Topic-level differences in comment sentiment were also observed: comments responding to posts on scientific- and policy-related debates (Topic 8) were more positive (Vargha-Delaney A=0.531), whereas those on systemic failures (Topic 4) were more negative (Vargha-Delaney A=0.478). Conclusions: Overall, the findings highlight how audience reactions can amplify emotionally charged narratives and reframe technical information into socially and politically charged debates. These insights can inform public health communication strategies by anticipating likely audience responses, mitigating stigma and misinformation, and fostering constructive dialogue during health crises.

Indexed as

CommunicationDisease OutbreaksMpox, MonkeypoxHumansaudience engagementinfectious outbreakmonkeypoxnatural language processingpublic discourseRedditsentiment analysissocial mediatopic modellingvirus

Identifiers

PMID42335474
PMCPMC13290164

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.