Evidence map›Paper›PMID 34255783›Full record

ArticlePloS one2021

Anatomy into the battle of supporting or opposing reopening amid the COVID-19 pandemic on Twitter: A temporal and spatial analysis.

Lingyao Li, Abdolmajid Erfani, Yu Wang, Qingbin Cui

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Public Health Policy, Political Ideology, and Public Emotion Related to COVID-19 in the U.S.International journal of environmental research and public health · 2023
    Article
  3. 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

4 authors.

Lingyao LiDepartment of Civil and Environmental Engineering, A. James Clark School of Engineering, University of Maryland, College Park, Maryland, United States of America.ORCID 0000-0001-5888-8311
Abdolmajid ErfaniDepartment of Civil and Environmental Engineering, A. James Clark School of Engineering, University of Maryland, College Park, Maryland, United States of America.ORCID 0000-0002-4703-1248
Yu WangDepartment of Civil and Environmental Engineering, A. James Clark School of Engineering, University of Maryland, College Park, Maryland, United States of America.ORCID 0000-0002-8768-1093
Qingbin CuiDepartment of Civil and Environmental Engineering, A. James Clark School of Engineering, University of Maryland, College Park, Maryland, United States of America.ORCID 0000-0001-8128-8883

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reopening amid the COVID-19 pandemic has triggered a battle on social media. The supporters perceived that the lockdown policy could damage the economy and exacerbate social inequality. By contrast, the opponents believed it was necessary to contain the spread and ensure a safe environment for recovery. Anatomy into the battle is of importance to address public concerns, beliefs, and values, thereby enabling policymakers to determine the appropriate solutions to implement reopening policy. To this end, we investigated over 1.5 million related Twitter postings from April 17 to May 30, 2020. With the aid of natural language processing (NLP) techniques and machine learning classifiers, we classified each tweet into either a "supporting" or "opposing" class and then investigated the public perception from temporal and spatial perspectives. From the temporal dimension, we found that both political and scientific news that were extensively discussed on Twitter led to the perception of opposing reopening. Further, being the first mover with full reopen adversely affected the public reaction to reopening policy, while being the follower or late mover resulted in positive responses. From the spatial dimension, the correlation and regression analyses suggest that the state-level perception was very likely to be associated with political affiliation and health value.

Indexed as

AttitudeCOVID-19HumansMachine LearningNatural Language ProcessingPoliticsQuarantineSocial MediaSpatio-Temporal Analysis

Identifiers

PMID34255783
PMCPMC8277023

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.