Evidence map›Paper›PMID 37163694›Full record

ArticleJournal of medical Internet research2023

Misinformation and Public Health Messaging in the Early Stages of the Mpox Outbreak: Mapping the Twitter Narrative With Deep Learning.

Andy Edinger, Danny Valdez, Eric Walsh-Buhi, Jennifer S Trueblood, Lorenzo Lorenzo-Luaces, Lauren A Rutter, Johan Bollen

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

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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

7 authors.

Andy EdingerCenter for Social and Biomedical Complexity, Indiana University, Bloomington, IN, United States.ORCID 0000-0001-8159-2121
Danny ValdezDepartment of Applied Health Science, School of Public Health, Indiana University, Bloomington, IN, United States.ORCID 0000-0002-2355-9881
Eric Walsh-BuhiDepartment of Applied Health Science, School of Public Health, Indiana University, Bloomington, IN, United States.ORCID 0000-0002-1690-5352
Jennifer S TruebloodCognitive Science Program, Indiana University, Bloomington, IN, United States.ORCID 0000-0002-9019-6574
Lorenzo Lorenzo-LuacesDepartment of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States.ORCID 0000-0002-8882-0243
Lauren A RutterDepartment of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States.ORCID 0000-0002-8852-7602
Johan BollenCenter for Social and Biomedical Complexity, Indiana University, Bloomington, IN, United States.ORCID 0000-0001-7031-9293

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundShortly after the worst of the COVID-19 pandemic, an outbreak of mpox introduced another critical public health emergency. Like the COVID-19 pandemic, the mpox outbreak was characterized by a rising prevalence of public health misinformation on social media, through which many US adults receive and engage with news. Digital misinformation continues to challenge the efforts of public health officials in providing accurate and timely information to the public. We examine the evolving topic distributions of social media narratives during the mpox outbreak to map the tension between rapidly diffusing misinformation and public health communication.

objectiveThis study aims to observe topical themes occurring in a large-scale collection of tweets about mpox using deep learning.

methodsWe leveraged a data set comprised of all mpox-related tweets that were posted between May 7, 2022, and July 23, 2022. We then applied Sentence Bidirectional Encoder Representations From Transformers (S-BERT) to the content of each tweet to generate a representation of its content in high-dimensional vector space, where semantically similar tweets will be located closely together. We projected the set of tweet embeddings to a 2D map by applying principal component analysis and Uniform Manifold Approximation Projection (UMAP). Finally, we group these data points into 7 topical clusters using k-means clustering and analyze each cluster to determine its dominant topics. We analyze the prevalence of each cluster over time to evaluate longitudinal thematic changes.

resultsOur deep-learning pipeline revealed 7 distinct clusters of content: (1) cynicism, (2) exasperation, (3) COVID-19, (4) men who have sex with men, (5) case reports, (6) vaccination, and (7) World Health Organization (WHO). Clusters that largely communicated erroneous or irrelevant information began earlier and grew faster, reaching a wider audience than later communications by official instances and health officials.

conclusionsWithin a few weeks of the first reported mpox cases, an avalanche of mostly false, misleading, irrelevant, or damaging information started to circulate on social media. Official institutions, including the WHO, acted promptly, providing case reports and accurate information within weeks, but were overshadowed by rapidly spreading social media chatter. Our results point to the need for real-time monitoring of social media content to optimize responses to public health emergencies.

Indexed as

COVID-19Deep LearningHealth CommunicationMpox, MonkeypoxSocial MediaAdultDisease OutbreaksHomosexuality, MaleHumansMalePandemicsPublic HealthSexual and Gender MinoritiesCOVID-19deep learningmisinformationmonkeypoxmpoxoutbreakpublic healthsocial mediaTwitter

Identifiers

PMID37163694
PMCPMC10282910

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.