Evidence map›Paper›PMID 30467106›Full record

ArticleJMIR public health and surveillance2018

Dynamics of Health Agency Response and Public Engagement in Public Health Emergency: A Case Study of CDC Tweeting Patterns During the 2016 Zika Epidemic.

Shi Chen, Qian Xu, John Buchenberger, Arunkumar Bagavathi, Gabriel Fair, Samira Shaikh, Siddharth Krishnan

Open access · goldAbstract read
In one paragraph

Article in JMIR public health and surveillance, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
4.1field-weighted citation impact, top 5% 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

21 citing papers in PubMed, 42 citations in OpenAlex.

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  17. Infodemiology and Infoveillance: Scoping Review.Journal of medical Internet research · 2020
    Article
  18. Tracking COVID-19 in Europe: Infodemiology Approach.JMIR public health and surveillance · 2020
    Article
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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 2 institutions in 1 country.

Shi Chen *Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC, United States.ORCID http://orcid.org/0000-0002-2316-111X
Qian XuSchool of Communications, Elon University, Elon, NC, United States.ORCID http://orcid.org/0000-0002-2354-0208
John BuchenbergerDepartment of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC, United States.ORCID http://orcid.org/0000-0002-8247-0342
Arunkumar BagavathiDepartment of Computer Sciences, University of North Carolina at Charlotte, Charlotte, NC, United States.ORCID http://orcid.org/0000-0002-7135-4602
Gabriel FairDepartment of Computer Sciences, University of North Carolina at Charlotte, Charlotte, NC, United States.ORCID http://orcid.org/0000-0002-0560-9558
Samira ShaikhDepartment of Computer Sciences, University of North Carolina at Charlotte, Charlotte, NC, United States.ORCID http://orcid.org/0000-0002-2488-9436
Siddharth KrishnanDepartment of Computer Sciences, University of North Carolina at Charlotte, Charlotte, NC, United States.ORCID http://orcid.org/0000-0002-9570-0186
University of North Carolina at Charlotte · USElon University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSocial media have been increasingly adopted by health agencies to disseminate information, interact with the public, and understand public opinion. Among them, the Centers for Disease Control and Prevention (CDC) is one of the first US government health agencies to adopt social media during health emergencies and crisis. It had been active on Twitter during the 2016 Zika epidemic that caused 5168 domestic noncongenital cases in the United States.

objectiveThe aim of this study was to quantify the temporal variabilities in CDC's tweeting activities throughout the Zika epidemic, public engagement defined as retweeting and replying, and Zika case counts. It then compares the patterns of these 3 datasets to identify possible discrepancy among domestic Zika case counts, CDC's response on Twitter, and public engagement in this topic.

methodsAll of the CDC-initiated tweets published in 2016 with corresponding retweets and replies were collected from 67 CDC-associated Twitter accounts. Both univariate and multivariate time series analyses were performed in each quarter of 2016 for domestic Zika case counts, CDC tweeting activities, and public engagement in the CDC-initiated tweets.

resultsCDC sent out >84.0% (5130/6104) of its Zika tweets in the first quarter of 2016 when Zika case counts were low in the 50 US states and territories (only 560/5168, 10.8% cases and 662/38,885, 1.70% cases, respectively). While Zika case counts increased dramatically in the second and third quarters, CDC efforts on Twitter substantially decreased. The time series of public engagement in the CDC-initiated tweets generally differed among quarters and from that of original CDC tweets based on autoregressive integrated moving average model results. Both original CDC tweets and public engagement had the highest mutual information with Zika case counts in the second quarter. Furthermore, public engagement in the original CDC tweets was substantially correlated with and preceded actual Zika case counts.

conclusionsConsiderable discrepancies existed among CDC's original tweets regarding Zika, public engagement in these tweets, and actual Zika epidemic. The patterns of these discrepancies also varied between different quarters in 2016. CDC was much more active in the early warning of Zika, especially in the first quarter of 2016. Public engagement in CDC's original tweets served as a more prominent predictor of actual Zika epidemic than the number of CDC's original tweets later in the year.

Indexed as

Centers for Disease Control and Preventioninfodemiologyinfoveillancepublic engagementsocial mediatime series analysistwitterTwitterZika epidemic

Identifiers

PMID30467106
PMCPMC6284147
OpenAlexW2891031432

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.