{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T06:01:10Z","timestamp":1792735270248,"version":"4.5.0"},"reference-count":36,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Public health crises often reshape online discourse by amplifying uncertainty, frustration, stigma, and misinformation, with important implications for risk communication.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study examines these dynamics on Reddit (Reddit Inc) during a recent outbreak, using Mpox as a case study.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Engagement differed significantly by sentiment (\n P<\/jats:italic>\n <.001) and topic (\n P<\/jats:italic>\n <.001). Negative posts had higher median scores (median 7, IQR 2-27) than positive ones (median 5, IQR 2-16;\n z<\/jats:italic>\n score=6.02; adjusted\n P<\/jats:italic>\n <.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).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/90152","type":"journal-article","created":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T21:47:02Z","timestamp":1782251222000},"page":"e90152-e90152","source":"Crossref","is-referenced-by-count":2,"title":["Reddit Discussions During the 2022 Mpox Outbreak: Observational Analysis of Sentiment, Topics, and Audience Engagement"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5954-7525","authenticated-orcid":false,"given":"Xi Ning","family":"Luo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5528-638X","authenticated-orcid":false,"given":"Zahra","family":"Movahedi Nia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7557-5672","authenticated-orcid":false,"given":"Jude Dzevela","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,6,23]]},"reference":[{"issue":"9","key":"R1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.ppat.1010768","article-title":"Human monkeypox: a review of the literature","volume":"18","author":"El Eid","journal-title":"PLOS Pathog"},{"issue":"9","key":"R2","doi-asserted-by":"publisher","DOI":"10.2196\/32685","article-title":"Comparing public sentiment toward COVID-19 vaccines across Canadian cities: analysis of comments on Reddit","volume":"23","author":"Yan","journal-title":"J Med Internet Res"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.2196\/45108","article-title":"Mpox panic, infodemic, and stigmatization of the two-spirit, lesbian, gay, bisexual, transgender, queer or questioning, intersex, asexual community: geospatial analysis, topic modeling, and sentiment analysis of a large, multilingual social media database","volume":"25","author":"Movahedi Nia","journal-title":"J Med Internet Res"},{"issue":"6","key":"R4","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1007\/s11524-023-00773-4","article-title":"Mpox on Reddit: a thematic analysis of online posts on mpox on a social media platform among key populations","volume":"100","author":"Hong","journal-title":"J Urban Health"},{"key":"R5","doi-asserted-by":"publisher","DOI":"10.1016\/j.puhe.2023.02.018","article-title":"Public discourse and sentiment during MPOX outbreak: an analysis using natural language processing","volume":"218","author":"Anoop","journal-title":"Public Health (Fairfax)"},{"key":"R6","doi-asserted-by":"publisher","DOI":"10.2196\/43841","article-title":"Misinformation and public health messaging in the early stages of the mpox outbreak: mapping the Twitter narrative with deep learning","volume":"25","author":"Edinger","journal-title":"J Med Internet Res"},{"key":"R7","doi-asserted-by":"publisher","DOI":"10.2196\/52997","article-title":"Toxicity on social media during the 2022 mpox public health emergency: quantitative study of topical and network dynamics","volume":"26","author":"Fan","journal-title":"J Med Internet Res"},{"issue":"1","key":"R8","doi-asserted-by":"publisher","DOI":"10.1136\/bmjph-2023-000407","article-title":"Social media as a public health tool during the UK mpox outbreak: a qualitative study of stakeholders\u2019 experiences","volume":"1","author":"Garcia-Iglesias","journal-title":"BMJ Public Health"},{"key":"R9","doi-asserted-by":"publisher","DOI":"10.2196\/68200","article-title":"Chicago public health department social media communications on Twitter during the COVID-19 pandemic and the mpox epidemic: cross-sectional content analysis","volume":"27","author":"Boyce","journal-title":"J Med Internet Res"},{"issue":"4","key":"R10","doi-asserted-by":"publisher","DOI":"10.1177\/20563051221130282","article-title":"Comments, shares, or likes: what makes news posts engaging in different ways","volume":"8","author":"Tenenboim","journal-title":"Social Media + Society"},{"key":"R11","unstructured":"R\/monkeypox. 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