Evidence map›Paper›PMID 37035497›Full record

ArticleOpen forum infectious diseases2023

Analyzing an Emerging Pandemic on Twitter: Monkeypox.

Lauren N Cooper, Alexander P Radunsky, John J Hanna, Zachary M Most, Trish M Perl, Christoph U Lehmann, Richard J Medford

Open access · goldAbstract read
In one paragraph

Article in Open forum infectious diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
13.3field-weighted citation impact, top 1% of its field
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

7 citing papers in PubMed, 23 citations in OpenAlex.

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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 at 1 institution in 1 country.

Lauren N CooperClinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.ORCID https://orcid.org/0000-0003-4000-9192
Alexander P RadunskyDepartment of Internal Medicine, Division of Infectious Diseases and Geographic Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
John J HannaClinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Zachary M MostDepartment of Pediatrics, Division of Pediatric Infectious Disease, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Trish M PerlDepartment of Internal Medicine, Division of Infectious Diseases and Geographic Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Christoph U LehmannClinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Richard J MedfordClinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
The University of Texas Southwestern Medical Center · US

Funding

UT Southwestern Center for Translational MedicineUL1TR003163 · NCATS · UT SOUTHWESTERN MEDICAL CENTER · PI TOTO, ROBERT DANIEL · 2021 to 2025
$39.3M
NCATS NIH HHS UL1 TR003163
6 · The paper itself

Abstract

Background: Social media platforms like Twitter provide important insights into the public's perceptions of global outbreaks like monkeypox. By analyzing tweets, we aimed to identify public knowledge and opinions on the monkeypox virus and related public health issues. Methods: We analyzed English-language tweets using the keyword "monkeypox" from 1 May to 23 July 2022. We reported gender, ethnicity, and race of Twitter users and analyzed tweets to identify predominant sentiment and emotions. We performed topic modeling and compared cohorts of users who self-identify as LGBTQ+ (an abreviation for lesbian, gay, bisexual, transgender, queer, and/or questioning) allies versus users who do not, and cohorts identified as "bots" versus humans. Results: A total of 48 330 tweets were written by LGBTQ+ self-identified advocates or allies. The mean sentiment score for all tweets was -0.413 on a -4 to +4 scale. Negative tweets comprised 39% of tweets. The most common emotions expressed were fear and sadness. Topic modeling identified unique topics among the 4 cohorts analyzed. Conclusions: The spread of mis- and disinformation about monkeypox was common in our tweet library. Various conspiracy theories about the origins of monkeypox, its relationship to global economic concerns, and homophobic and racial comments were common. Conversely, many other tweets helped to provide information about monkeypox vaccines, disease symptoms, and prevention methods. Discussion of rising monkeypox case numbers globally was also a large aspect of the conversation. Conclusions: We demonstrated that Twitter is an effective means of tracking sentiment about public healthcare issues. We gained insight into a subset of people, self-identified LGBTQ+ allies, who were more affected by monkeypox.

Indexed as

monkeypoxpandemicsocial mediatopic modelingTwitter

Identifiers

PMID37035497
PMCPMC10077829
OpenAlexW4327723529

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.