Evidence map›Paper›PMID 34608258›Full record

ArticleScientific reports2021

Clusters of science and health related Twitter users become more isolated during the COVID-19 pandemic.

Francesco Durazzi, Martin Müller, Marcel Salathé, Daniel Remondini

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Anti-science and the science community.Nature reviews. Microbiology · 2025
    Article
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  8. Effects of media on preventive behaviour during the COVID-19 pandemic.Humanities & social sciences communications · 2023
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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.

Francesco Durazzi *Department of Astronomy and Physics (DIFA), University of Bologna, 40127, Bologna, Italy. francesco.durazzi2@unibo.it.
Martin Müller *Digital Epidemiology Lab, Ecole polytechnique fédérale de Lausanne (EPFL), 1202, Geneva, Switzerland.
Marcel SalathéDigital Epidemiology Lab, Ecole polytechnique fédérale de Lausanne (EPFL), 1202, Geneva, Switzerland.
Daniel RemondiniDepartment of Astronomy and Physics (DIFA), University of Bologna, 40127, Bologna, Italy.

Funding

H2020 Societal Challenges 874735
6 · The paper itself

Abstract

COVID-19 represents the most severe global crisis to date whose public conversation can be studied in real time. To do so, we use a data set of over 350 million tweets and retweets posted by over 26 million English speaking Twitter users from January 13 to June 7, 2020. We characterize the retweet network to identify spontaneous clustering of users and the evolution of their interaction over time in relation to the pandemic's emergence. We identify several stable clusters (super-communities), and are able to link them to international groups mainly involved in science and health topics, national elites, and political actors. The science- and health-related super-community received disproportionate attention early on during the pandemic, and was leading the discussion at the time. However, as the pandemic unfolded, the attention shifted towards both national elites and political actors, paralleled by the introduction of country-specific containment measures and the growing politicization of the debate. Scientific super-community remained present in the discussion, but experienced less reach and became more isolated within the network. Overall, the emerging network communities are characterized by an increased self-amplification and polarization. This makes it generally harder for information from international health organizations or scientific authorities to directly reach a broad audience through Twitter for prolonged time. These results may have implications for information dissemination along the unfolding of long-term events like epidemic diseases on a world-wide scale.

Indexed as

Social IsolationSocial MediaCOVID-19HumansPandemicsPoliticsSARS-CoV-2Social Network AnalysisSocial Networking

Identifiers

PMID34608258
PMCPMC8490394

What OpenQuestion holds

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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.