Evidence map›Paper›PMID 33134595›Full record

ArticleJournal of computational social science2020

Conspiracy in the time of corona: automatic detection of emerging COVID-19 conspiracy theories in social media and the news.

Shadi Shahsavari, Pavan Holur, Tianyi Wang, Timothy R Tangherlini, Vwani Roychowdhury

Open access · bronzeAbstract read
In one paragraph

Article in Journal of computational social science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
79citing papers in PubMed, 3 pooled it
69.8field-weighted citation impact, top 1% 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

79 citing papers in PubMed, 3 syntheses or guidelines pooled it, 277 citations in OpenAlex.

  1. Pooled it
  2. When Infodemic Meets Epidemic: Systematic Literature Review.JMIR public health and surveillance · 2025
    Pooled it
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  10. Navigating parental attitudes on childhood vaccination in Jordan: a cross-sectional study.International journal of environmental health research · 2025
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19 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 at 2 institutions in 1 country.

Shadi ShahsavariElectrical and Computer Engineering, UCLA, Los Angeles, CA USA.ORCID 0000-0003-0169-3403
Pavan HolurElectrical and Computer Engineering, UCLA, Los Angeles, CA USA.ORCID 0000-0002-8495-9416
Tianyi WangElectrical and Computer Engineering, UCLA, Los Angeles, CA USA.ORCID 0000-0002-7263-5852
Timothy R TangherliniDepartment of Scandinavian, University of California, Berkeley, CA USA.ORCID 0000-0002-1775-2052
Vwani RoychowdhuryElectrical and Computer Engineering, UCLA, Los Angeles, CA USA.ORCID 0000-0003-0832-6489
UCLA Health · USUniversity of California, Berkeley · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rumors and conspiracy theories thrive in environments of low confidence and low trust. Consequently, it is not surprising that ones related to the COVID-19 pandemic are proliferating given the lack of scientific consensus on the virus's spread and containment, or on the long-term social and economic ramifications of the pandemic. Among the stories currently circulating in US-focused social media forums are ones suggesting that the 5G telecommunication network activates the virus, that the pandemic is a hoax perpetrated by a global cabal, that the virus is a bio-weapon released deliberately by the Chinese, or that Bill Gates is using it as cover to launch a broad vaccination program to facilitate a global surveillance regime. While some may be quick to dismiss these stories as having little impact on real-world behavior, recent events including the destruction of cell phone towers, racially fueled attacks against Asian Americans, demonstrations espousing resistance to public health orders, and wide-scale defiance of scientifically sound public mandates such as those to wear masks and practice social distancing, countermand such conclusions. Inspired by narrative theory, we crawl social media sites and news reports and, through the application of automated machine-learning methods, discover the underlying narrative frameworks supporting the generation of rumors and conspiracy theories. We show how the various narrative frameworks fueling these stories rely on the alignment of otherwise disparate domains of knowledge, and consider how they attach to the broader reporting on the pandemic. These alignments and attachments, which can be monitored in near real time, may be useful for identifying areas in the news that are particularly vulnerable to reinterpretation by conspiracy theorists. Understanding the dynamics of storytelling on social media and the narrative frameworks that provide the generative basis for these stories may also be helpful for devising methods to disrupt their spread.

Indexed as

4Chan5GBill GatesBio-weaponsChinaConspiracy theoriesCorona virusCOVID-19Data visualizationMachine learningNarrativeNetworksNewsRedditRumorSocial media

Identifiers

PMID33134595
PMCPMC7591696
OpenAlexW3097252853

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.