Evidence map›Paper›PMID 32982038›Full record

ArticleComputers in human behavior2021

Examining risk and crisis communications of government agencies and stakeholders during early-stages of COVID-19 on Twitter.

Yan Wang, Haiyan Hao, Lisa Sundahl Platt

Abstract read
In one paragraph

Article in Computers in human behavior, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
70citing 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

70 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. Communicating Risk in the Age of Misinformation: Empirical Frameworks for Understanding Stakeholder Trust.Risk analysis : an official publication of the Society for Risk Analysis · 2026
    Article
  3. Article
  4. Planning and Implementing Social Media Communication in a Public Health Crisis: An Analytical Perspective.Health promotion journal of Australia : official journal of Australian Association of Health Promotion Professionals · 2026
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10 more citing papers are in PubMed but not listed here.

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

3 authors.

Yan WangDepartment of Urban and Regional Planning and Florida Institute for Built Environment Resilience, University of Florida, P.O. Box 115706, Gainesville, FL, 32611, USA.
Haiyan HaoDepartment of Urban and Regional Planning and Florida Institute for Built Environment Resilience, College of Design, Construction and Planning, University of Florida, 1480 Inner Road, Gainesville, FL, 32601, USA.
Lisa Sundahl PlattDepartment of Interior Design and Florida Institute for Built Environment Resilience, University of Florida, P.O. Box 115701, Gainesville, FL, 32611, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During COVID-19, social media has played an important role for public health agencies and government stakeholders (i.e. actors) to disseminate information regarding situations, risks, and personal protective action inhibiting disease spread. However, there have been notable insufficient, incongruent, and inconsistent communications regarding the pandemic and its risks, which was especially salient at the early stages of the outbreak. Sufficiency, congruence and consistency in health risk communication have important implications for effective health safety instruction as well as critical content interpretability and recall. It also impacts individual- and community-level responses to information. This research employs text mining techniques and dynamic network analysis to investigate the actors' risk and crisis communication on Twitter regarding message types, communication sufficiency, timeliness, congruence, consistency and coordination. We studied 13,598 pandemic-relevant tweets posted over January to April from 67 federal and state-level agencies and stakeholders in the U.S. The study annotates 16 categories of message types, analyzes their appearances and evolutions. The research then identifies inconsistencies and incongruencies on four critical topics and examines spatial disparities, timeliness, and sufficiency across actors and message types in communicating COVID-19. The network analysis also reveals increased communication coordination over time. The findings provide unprecedented insight of Twitter COVID-19 information dissemination which may help to inform public health agencies and governmental stakeholders future risk and crisis communication strategies related to global hazards in digital environments.

Indexed as

COVID-19Crisis risk communicationNetwork analysisPublic healthSocial media

Identifiers

PMID32982038
PMCPMC7508681

What OpenQuestion holds

Textmetadata
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.