Evidence map›Paper›PMID 32838229›Full record

ArticleHuman behavior and emerging technologies2020

Political polarization drives online conversations about COVID-19 in the United States.

Julie Jiang, Emily Chen, Shen Yan, Kristina Lerman, Emilio Ferrara

Abstract read
In one paragraph

Article in Human behavior and emerging technologies, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed, 2 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

63 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  6. Political identity and risk politics: Evidence from a pandemic.Risk analysis : an official publication of the Society for Risk Analysis · 2025
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3 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

5 authors.

Julie JiangUSC Information Sciences Institute University of Southern California Los Angeles California USA.ORCID https://orcid.org/0000-0003-4260-282X
Emily ChenUSC Information Sciences Institute University of Southern California Los Angeles California USA.
Shen YanUSC Information Sciences Institute University of Southern California Los Angeles California USA.
Kristina LermanUSC Information Sciences Institute University of Southern California Los Angeles California USA.
Emilio FerraraUSC Information Sciences Institute University of Southern California Los Angeles California USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the outbreak in China in late 2019, the novel coronavirus (COVID-19) has spread around the world and has come to dominate online conversations. By linking 2.3 million Twitter users to locations within the United States, we study in aggregate how political characteristics of the locations affect the evolution of online discussions about COVID-19. We show that COVID-19 chatter in the United States is largely shaped by political polarization. Partisanship correlates with sentiment toward government measures and the tendency to share health and prevention messaging. Cross-ideological interactions are modulated by user segregation and polarized network structure. We also observe a correlation between user engagement with topics related to public health and the varying impact of the disease outbreak in different U.S. states. These findings may help inform policies both online and offline. Decision-makers may calibrate their use of online platforms to measure the effectiveness of public health campaigns, and to monitor the reception of national and state-level policies, by tracking in real-time discussions in a highly polarized social media ecosystem.

Indexed as

communication dynamicscontent analysiscoronavirusCOVID‐19geospatial analysisnetwork analysispartisanshippolarizationsocial media platformsuser behavior modeling

Identifiers

PMID32838229
PMCPMC7323338

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.