ArticlePloS one2024
Identifying and characterizing superspreaders of low-credibility content on Twitter.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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Who cites it
4 citing papers in PubMed.
- The role of social media influencer promotion in initiation and use of cigar, little cigar, cigarillo, and cannabis products among US youth and young adults: Using exogenous marketing exposure measures.Social science & medicine (1982) · 2026Article
- A coupled disease-misinformation model of measles transmission in the Canadian context.Infectious Disease Modelling · 2026Article
- Co-Designing a Framework for Social Media Health Communication to Young People: A Participatory Research Study.Health expectations : an international journal of public participation in health care and health policy · 2025Article
- Perceived experts are prevalent and influential within an antivaccine community on Twitter.PNAS nexus · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The world's digital information ecosystem continues to struggle with the spread of misinformation. Prior work has suggested that users who consistently disseminate a disproportionate amount of low-credibility content-so-called superspreaders-are at the center of this problem. We quantitatively confirm this hypothesis and introduce simple metrics to predict the top superspreaders several months into the future. We then conduct a qualitative review to characterize the most prolific superspreaders and analyze their sharing behaviors. Superspreaders include pundits with large followings, low-credibility media outlets, personal accounts affiliated with those media outlets, and a range of influencers. They are primarily political in nature and use more toxic language than the typical user sharing misinformation. We also find concerning evidence that suggests Twitter may be overlooking prominent superspreaders. We hope this work will further public understanding of bad actors and promote steps to mitigate their negative impacts on healthy digital discourse.
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Registered trials
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.