Evidence map›Paper›PMID 39908546›Full record

Observational studyJournal of medical Internet research2025

Public Health Messaging on Twitter During the COVID-19 Pandemic: Observational Study.

Ashwin Rao, Nazanin Sabri, Siyi Guo, Louiqa Raschid, Kristina Lerman

Abstract readObservational Study
In one paragraph

Observational study in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

5 authors.

Ashwin Rao *Information Sciences Institute, University of Southern California, Marina del Rey, CA, United States.ORCID 0000-0001-8751-3076
Nazanin Sabri *Department of Computer Science and Engineering, University of California - San Diego, La Jolla, CA, United States.ORCID 0000-0002-0861-9444
Siyi GuoInformation Sciences Institute, University of Southern California, Marina del Rey, CA, United States.ORCID 0000-0001-6422-3420
Louiqa RaschidInstitute of Advanced Computer Studies, University of Maryland, College Park, MD, United States.ORCID 0000-0002-0630-6049
Kristina LermanInformation Sciences Institute, University of Southern California, Marina del Rey, CA, United States.ORCID 0000-0002-5071-0575

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEffective communication is crucial during health crises, and social media has become a prominent platform for public health experts (PHEs) to share information and engage with the public. At the same time, social media also provides a platform for pseudoexperts who may spread contrarian views. Despite the importance of social media, key elements of communication, such as the use of moral or emotional language and messaging strategy, particularly during the emergency phase of the COVID-19 pandemic, have not been explored.

objectiveThis study aimed to analyze how PHEs and pseudoexperts communicated with the public during the emergency phase of the COVID-19 pandemic. We focused on the emotional and moral language used in their messages on various COVID-19 pandemic-related topics. We also analyzed their interactions with political elites and the public's engagement with PHEs to gain a deeper understanding of their influence on public discourse.

methodsFor this observational study, we gathered a dataset of >539,000 original posts or reposts from 489 PHEs and 356 pseudoexperts on Twitter (subsequently rebranded X) from January 2020 to January 2021, along with the replies to the original posts from the PHEs. We identified the key issues that PHEs and pseudoexperts prioritized. We also determined the emotional and moral language in both the original posts and the replies. This allows us to characterize priorities for PHEs and pseudoexperts as well as differences in messaging strategy between these 2 groups. We also evaluated the influence of PHEs' language and strategy on the public response.

resultsOur analyses revealed that PHEs focused more on masking, health care, education, and vaccines, whereas pseudoexperts discussed therapeutics and lockdowns more frequently (P<.001). PHEs typically used positive emotional language across all issues (P<.001), expressing optimism and joy. Pseudoexperts often used negative emotions of pessimism and disgust, while limiting positive emotional language to origins and therapeutics (P<.001). Along the dimensions of moral language, PHEs and pseudoexperts differed on care versus harm and authority versus subversion across different issues. Negative emotional and moral language tends to boost engagement in COVID-19 discussions across all issues. However, the use of positive language by PHEs increases the use of positive language in the public responses. PHEs act as liberal partisans: they express more positive affect in their posts directed at liberals and more negative affect in their posts directed at conservative elites. In contrast, pseudoexperts act as conservative partisans. These results provide nuanced insights into the elements that have polarized the COVID-19 discourse.

conclusionsUnderstanding the nature of the public response to PHEs' messages on social media is essential for refining communication strategies during health crises. Our findings underscore the importance of using moral-emotional language strategically to reduce polarization and build trust.

Indexed as

COVID-19Public HealthSocial MediaCommunicationHumansPandemicsSARS-CoV-2COVID-19emotionsmoral foundationspolarizationpublic healthpublic health messagingTwitter

Identifiers

PMID39908546
PMCPMC11840374

What OpenQuestion holds

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LicenceCC BY
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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.