ArticlePloS one2018
Anatomy of an online misinformation network.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis 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
40 citing papers in PubMed, 1 synthesis or guideline pooled it, 272 citations in OpenAlex.
- Systematic Literature Review on the Spread of Health-related Misinformation on Social Media.Social science & medicine (1982) · 2019Pooled it
- Tracking evolving communities in fake news cascades using temporal graphs.Scientific reports · 2026Article
- Quantifying the vulnerabilities of the online public square to adversarial manipulation tactics.PNAS nexus · 2024Article
- A multilingual analysis of pro Russian misinformation on Twitter during the Russian invasion of Ukraine.Scientific reports · 2024Article
- Flexible Bayesian inference on partially observed epidemics.Journal of complex networks · 2024Article
- [Mis- and disinformation in social media: mitigating risks in digital health communication].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2024Review
- Comparing methods for creating a national random sample of twitter users.Social network analysis and mining · 2024Article
- Article
- (Why) Is Misinformation a Problem?Perspectives on psychological science : a journal of the Association for Psychological Science · 2023Article
- Tokenization of social media engagements increases the sharing of false (and other) news but penalization moderates it.Scientific reports · 2023Article
- Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections.Nature human behaviour · 2023Article
- One Year of COVID-19 Vaccine Misinformation on Twitter: Longitudinal Study.Journal of medical Internet research · 2023Article
- Social Bots' Role in the COVID-19 Pandemic Discussion on Twitter.International journal of environmental research and public health · 2023Article
- A survey of uncover misleading and cyberbullying on social media for public health.Cluster computing · 2023Review
- Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques.PeerJ. Computer science · 2023Article
- Article
- Fake news, disinformation and misinformation in social media: a review.Social network analysis and mining · 2023Review
- Digital Resilience in Dealing with Misinformation on Social Media during COVID-19: A Web Application to Assist Users in Crises.Information systems frontiers : a journal of research and innovation · 2022Article
- The Influence of Provaping "Gatewatchers" on the Dissemination of COVID-19 Misinformation on Twitter: Analysis of Twitter Discourse Regarding Nicotine and the COVID-19 Pandemic.Journal of medical Internet research · 2022Article
- Online misinformation is linked to early COVID-19 vaccination hesitancy and refusal.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy captures public tweets that include links to articles from low-credibility and fact-checking sources. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network.
Indexed as
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.