Evidence map›Paper›PMID 40801010›Full record

ArticleIEEE access : practical innovations, open solutions2022

Analyzing Tweeting Patterns and Public Engagement on Twitter During the Recognition Period of the COVID-19 Pandemic: A Study of Two U.S. States.

Misbah Ul Hoque, Kisung Lee, Jessica L Beyer, Sara R Curran, Katie S Gonser, Nina S N Lam, Volodymyr V Mihunov, Kejin Wang

Abstract read
In one paragraph

Article in IEEE access : practical innovations, open solutions, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. When Infodemic Meets Epidemic: Systematic Literature Review.JMIR public health and surveillance · 2025
    Pooled it
  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

8 authors.

Misbah Ul HoqueDivision of Computer Science and Engineering, Louisiana State University, Baton Rouge, LA 70803, USA.ORCID 0000-0001-9354-7838
Kisung LeeDivision of Computer Science and Engineering, Louisiana State University, Baton Rouge, LA 70803, USA.ORCID 0000-0003-4367-4374
Jessica L BeyerThe Henry M. Jackson School of International Studies, University of Washington, Seattle, WA 98195, USA.
Sara R CurranThe Henry M. Jackson School of International Studies, University of Washington, Seattle, WA 98195, USA.ORCID 0000-0001-9353-4287
Katie S GonserThe Henry M. Jackson School of International Studies, University of Washington, Seattle, WA 98195, USA.
Nina S N LamDepartment of Environmental Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.ORCID 0000-0002-5344-9368
Volodymyr V MihunovDepartment of Environmental Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.
Kejin WangDepartment of Environmental Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.ORCID 0000-0001-8736-4955

Funding

Scientific/Technical CoreP2CHD042828 · NICHD · UNIVERSITY OF WASHINGTON · PI SARA R. CURRAN · 2017 to 2026
$6.3M
NICHD NIH HHS P2C HD042828
6 · The paper itself

Abstract

The abundance of available information on social media can provide invaluable insights into people's responses to health information and public health guidance concerning COVID-19. This study examines tweeting patterns and public engagement on Twitter, as forms of social media, related to public health messaging in two U.S. states (Washington and Louisiana) during the early stage of the pandemic. We analyze more than 7M tweets and 571K COVID-19-related tweets posted by users in the two states over the first 25 days of the pandemic in the U.S. (Feb. 23, 2020, to Mar. 18, 2020). We also qualitatively code and examine 460 tweets posted by selected governmental official accounts during the same period for public engagement analysis. We use various methods for analyzing the data, including statistical analysis, sentiment analysis, and word usage metrics, to find inter- and intra-state disparities of tweeting patterns and public engagement with health messaging. Our findings reveal that users in Washington were more active on Twitter than users in Louisiana in terms of the total number and density of COVID-19-related tweets during the early stage of the pandemic. Our correlation analysis results for counties or parishes show that the Twitter activities (tweet density, COVID-19 tweet density, and user density) were positively correlated with population density in both states at the 0.01 level of significance. Our sentiment analysis results demonstrate that the average daily sentiment scores of all and COVID-19-related tweets in Washington were consistently higher than those in Louisiana during this period. While the daily average sentiment scores of COVID-19-related tweets were in the negative range, the scores of all tweets were in the positive range in both states. Lastly, our analysis of governmental Twitter accounts found that these accounts' messages were most commonly meant to spread information about the pandemic, but that users were most likely to engage with tweets that requested readers take action, such as hand washing.

Indexed as

COVID-19geospatial data analysisnatural language processingpublic engagementpublic health messagingsentiment analysisstatistical analysisTwitter data analytics

Identifiers

PMID40801010
PMCPMC12341642

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.