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
Author Information
  1. Lauren N Cooper: Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA. ORCID
  2. Alexander P Radunsky: Department of Internal Medicine, Division of Infectious Diseases and Geographic Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  3. John J Hanna: Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  4. Zachary M Most: Department of Pediatrics, Division of Pediatric Infectious Disease, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  5. Trish M Perl: Department of Internal Medicine, Division of Infectious Diseases and Geographic Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  6. Christoph U Lehmann: Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  7. Richard J Medford: Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.

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.

Keywords

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Grants

  1. UL1 TR003163/NCATS NIH HHS

Word Cloud

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