ArticleNature communications2019
Influence of fake news in Twitter during the 2016 US presidential election.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 papers, 2 of them syntheses that pooled 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.
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.
Who cites it
109 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Physical Activity Misinformation on Social Media: Systematic Review.JMIR infodemiology · 2025Pooled it
- Disinformation as an obstructionist strategy in climate change mitigation: a review of the scientific literature for a systemic understanding of the phenomenon.Open research Europe · 2024Pooled it
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- Unpacking the Risk of Misinformation: A Communication-Based Critique.Risk analysis : an official publication of the Society for Risk Analysis · 2025Article
- Multifaceted polarization and information reliability in climate change discussions on social media platforms.Royal Society open science · 2025Article
- Article
- The benefits and dangers of anthropomorphic conversational agents.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Relative engagement with sources of climate misinformation is growing across social media platforms.Scientific reports · 2025Article
- Twitter communities are associated with changing user's opinion towards COVID-19 vaccine in Japan.Scientific reports · 2025Article
- Spreading dynamics of information on online social networks.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Differential impact from individual versus collective misinformation tagging on the diversity of Twitter (X) information engagement and mobility.Nature communications · 2025Article
- Evaluating the effect of viral posts on social media engagement.Scientific reports · 2025Article
- Article
- Article
- Rumor management in public health: a system dynamics analysis based on social trust.Frontiers in public health · 2025Article
- Modeling the amplification of epidemic spread by individuals exposed to misinformation on social media.Npj complexity · 2025Article
- Alternative Health and Conventional Medicine Discourse About Cancer on TikTok: Computer Vision Analysis of TikTok Videos.Journal of medical Internet research · 2024Article
- Patterns of partisan toxicity and engagement reveal the common structure of online political communication across countries.Nature communications · 2024Article
- Article
- Exploring agent interaction patterns in the comment sections of fake and real news.Journal of the Royal Society, Interface · 2024Article
49 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The dynamics and influence of fake news on Twitter during the 2016 US presidential election remains to be clarified. Here, we use a dataset of 171 million tweets in the five months preceding the election day to identify 30 million tweets, from 2.2 million users, which contain a link to news outlets. Based on a classification of news outlets curated by www.opensources.co , we find that 25% of these tweets spread either fake or extremely biased news. We characterize the networks of information flow to find the most influential spreaders of fake and traditional news and use causal modeling to uncover how fake news influenced the presidential election. We find that, while top influencers spreading traditional center and left leaning news largely influence the activity of Clinton supporters, this causality is reversed for the fake news: the activity of Trump supporters influences the dynamics of the top fake news spreaders.
Identifiers
What OpenQuestion holds
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.