The spread of true and false news online

2018/03/08 by Soroush Vosoughi, Deb Roy, Sinan Aral · 2 voices · 8,547 citations
Computer Science · Physics and Astronomy · Psychology · Social Sciences · #Computer science #Deception #Fake news #Internet privacy #Law #Misinformation and Its Impacts #Novelty #Opinion Dynamics and Social Influence #Political science #Politics #Psychology #Rumor #Set (abstract data type) #Social media #Social psychology #Spam and Phishing Detection

paper · doi:10.1126/science.aap9559

published in Science 359(6380), 1146-1151 (American Association for the Advancement of Science)

openalex publication_date 2018/03/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

Abstract

We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017. The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times. We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98% agreement on the classifications. Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information. We found that false news was more novel than true news, which suggests that people were more likely to share novel information. Whereas false stories inspired fear, disgust, and surprise in replies, true stories inspired anticipation, sadness, joy, and trust. Contrary to conventional wisdom, robots accelerated the spread of true and false news at the same rate, implying that false news spreads more than the truth because humans, not robots, are more likely to spread it.

Citations

• The Rise of Social Bots
• On Information and Sufficiency
• A Practitioner’s Guide to Cluster-Robust Inference
• What Makes Online Content Viral?

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• Fake News, Investor Attention, and Market Reaction
• Real-time Claim Detection from News Articles and Retrieval of Semantically-Similar Factchecks
• Misrepresenting Scientific Consensus on COVID-19: The Amplification of Dissenting Scientists on Twitter
• Simple changes to content curation algorithms affect the beliefs people form in a collaborative filtering experiment
• How did internet information influence COVID-19 vaccination? The cyclical influence of the internet information, beliefs, attitudes, and intentions toward vaccines from a three-wave longitudinal study
• The Diffusion and Reach of (Mis)Information on Facebook during the U.S. 2020 Election
• Retransmitting Messages on Social Media in Disasters
• The socio-psychological predictors of support for post-truth collective action
• Sharing of misinformation is habitual, not just lazy or biased
• The psychological drivers of misinformation belief and its resistance to correction
• Digital Capitalism and Distributive Forces (excerpt)
• IAT/ML: a metamodel and modelling approach for discourse analysis
• Containing misinformation: Modeling spatial games of fake news
• Inferring networks from time series: A neural approach
• The Evolution of Rumors on a Closed Platform during COVID-19
• Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users Based on Weakly Supervised Learning
• Community Fact-Checks Do Not Break Follower Loyalty
• SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval
• Mathematical Politics
• Online Conspiracy Theories, Digital Platforms and Secondary Orality: Toward a Sociology of Online Monsters
• Online conspiracy communities are more resilient to deplatforming
• Social sampling and expressed attitudes: Authenticity preference and social extremeness aversion lead to social norm effects and polarization.
• Beyond Likes: How Normative Feedback Complements Engagement Signals on Social Media
• The Niche Connectivity Paradox: Multichrome Contagions Overcome Vaccine Hesitancy more effectively than Monochromacy
• A political cartography of news sharing: Capturing story, outlet and content level of news circulation on Twitter
• Revisiting Information Diffusion Beyond Explicit Social Ties: A Study of Implicit-Link Diffusion on Twitter
• How to Deal with Fake News: Visualizing Disinformation
• Road to the White House: Analyzing the Relations Between Mainstream and Social Media During the U.S. Presidential Primaries
• Implementing Learning Content Management.
• Adapting Security Warnings to Counter Online Disinformation
• Unveiling critical nitrogen metabolism nodes and stakeholder priorities across coastal wetland eco-socioeconomic systems via integrated network analytics
• TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking
• Spectral Analysis of Fake News Propagation
• Sequential Modelling with Applications to Music Recommendation, Fact-Checking, and Speed Reading
• Hierarchical Synchronization and Distortion Scaling in Social Media Networks: A Fractal-Like Topology Theory
• Towards Explainable Fact Checking
• FNED
• False Information on Web and Social Media: A Survey
• Cascade-LSTM: Predicting Information Cascades using Deep Neural Networks
• Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents
• CAMOUFLAGE: Exploiting Misinformation Detection Systems Through LLM-driven Adversarial Claim Transformation
• The Great Data Standoff: Researchers vs. Platforms Under the Digital Services Act
• The Future of Misinformation Detection: New Perspectives and Trends
• Social Theory Should Be a Structural Prior for Agentic AI: A Formal Framework for Multi-Agent Social Systems
• Silence and Noise: Self-censorship and Opinion Expression on Social Media
• Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media
• Facts and Myths about Misperceptions
• Promises and Perils of Experimentation: The Mutual-Internal-Validity Problem
• Retweeting: its linguistic and epistemic value
• Beyond the "Truth": Investigating Election Rumors on Truth Social During the 2024 Election
• Perverse Downstream Consequences of Debunking: Being Corrected by Another User for Posting False Political News Increases Subsequent Sharing of Low Quality, Partisan, and Toxic Content in a Twitter Field Experiment
• Reason-checking fake news
• Unraveling Disinformation: The Case of Malaysia Airlines Flight MH17
• Beyond Fact-Checking: Network Analysis Tools for Monitoring Disinformation in Social Media
• The spread of low-credibility content by social bots
• Multilingual Multimodal Digital Deception Detection and Disinformation\n Spread across Social Platforms
• Fake News Early Detection: An Interdisciplinary Study
• Investigating Misinformation in Online Marketplaces: An Audit Study on Amazon
• Mistrust and misinformation: A two-component, socio-epistemic model of belief in conspiracy theories
• Linguistic Uncertainty and Engagement in Arabic-Language X (formerly Twitter) Discourse
• Robust Misinformation Detection by Visiting Potential Commonsense Conflict
• Simple Visual Artifact Detection in Sora-Generated Videos
• Measuring Google AI Overviews: Activation, Source Quality, Claim Fidelity, and Publisher Impact
• Architecture Sustained in Concrete The new San Francisco Federal Building
• The Digital Commons: Tragedy or Opportunity? A Reflection on the 50th Anniversary of Hardin’s Tragedy of the Commons
• Intelligent social bots uncover the link between user preference and diversity of news consumption
• Sources, Channels and Strategies of Disinformation in the 2020 US Election: Social Networks, Traditional Media and Political Candidates
• When Cow Urine Cures Constipation on YouTube: Limits of LLMs in Detecting Culture-specific Health Misinformation
• Attention Asymmetry in AI Layoff Discourse on X: A Computational Analysis of Capital vs Labour Amplification
• How to Detect Information Voids Using Longitudinal Data from Social Media and Web Searches
• The science of fake news
• The facts of fake news: A research review
• Less than you think: Prevalence and predictors of fake news dissemination on Facebook
• Hearing From Both Sides: Differences Between Liberal and Conservative Attitudes Toward Scientific and Experiential Evidence
• Fake news on Twitter during the 2016 U.S. presidential election
• Shades of fake news: how fallacies influence consumers’ perception
• Social media and moral panics: Assessing the effects of technological change on societal reaction
• Promoting Cooperation in the Public Goods Game using Artificial Intelligent Agents
• Arming the public with artificial intelligence to counter social bots
• La Infodemia del Cáncer en el Brasil: Una Mirada Crítica sobre Algunos Impactos de la Desinformación y Estrategias de Combate
• From the Selfish Gene to the “We”: Cooperation Often Needs Suppressors
• Collective communication and behaviour in response to uncertain ‘Danger’ in network experiments
• Avoiding virtual dystopia: A design theory for emancipatory participatory immersive platforms
• Large Language Models and Social Media Information Integrity: Opportunities, Challenges, and Research Directions
• Applications of Social Media in Hydroinformatics: A Survey
• Discussing Privacy and Surveillance on Twitter: A Case Study of COVID-19
• Exploring Lightweight Interventions at Posting Time to Reduce the Sharing of Misinformation on Social Media
• Unpacking the Social Media Bot: A Typology to Guide Research and Policy
• Bias and High-Dimensional Adjustment in Observational Studies of Peer Effects
• Online Social Deception and Its Countermeasures: A Survey
• The Effect of Social Media on Shaping Individuals Opinion Formation
• Automatically Appraising the Credibility of Vaccine-Related Web Pages Shared on Social Media: A Twitter Surveillance Study
• Misinformation technology: Internet use and political misperceptions in Africa
• Diffusion on Social Media Platforms: A Point Process Model for Interaction among Similar Content
• Transitions between polarization and radicalization in a temporal bilayer echo chambers model
• Artificial intelligence in the battle against disinformation and misinformation: a systematic review of challenges and approaches
• TweetsCOV19 - A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic
• False News Detection on Social Media
• VLM as Policy: Common-Law Content Moderation Framework for Short Video Platform
• Scalable and Generalizable Social Bot Detection through Data Selection
• Covid-19 Discourse on Twitter: How the Topics, Sentiments, Subjectivity, and Figurative Frames Changed Over Time
• Predicting Information Pathways Across Online Communities
• A new sociology of humans and machines
• Fake news agenda in the era of COVID-19: Identifying trends through fact-checking content
• Technology Dualities and Paradoxes in Digital Well-Being: Analysis of Discourses of Digital Well-Being Market Offerings
• Collaboration and Controversy Among Experts: Rumor Early Detection by Tuning a Comment Generator
• Blockchain and Trust in a Smart City
• Likelihood-based anomaly detection in preferential attachment networks
• Fact-Checking with Contextual Narratives: Leveraging Retrieval-Augmented LLMs for Social Media Analysis
• Authoritarian Recursions: How Fiction, History, and AI Reinforce Control in Education, Warfare, and Discourse
• Rethinking News and Media System Design Towards Positive Societal Implications
• MOSAIC: Modeling Social AI for Content Dissemination and Regulation in Multi-Agent Simulations
• Controlling Fairness and Bias in Dynamic Learning-to-Rank
• Can dialogues with AI systems help humans better discern visual misinformation?
• Exploiting stance hierarchies for cost-sensitive stance detection of Web documents
• Fake news [wikipedia]

Discussions

• フェイクニュースの性質と対策 偽情報・誤情報は正しい情報より拡散速度が速いことは知られています。 2006年から17年までにTwitterの投稿データを用いて、投稿されたフェイクニュースは正しいニュースの6倍の速さで拡散されていたことが明らかにされており、ユーザーの新規情報に対する好みや、怒りの情動反応が利用されていたことが指摘されています。 The spread of true and fal [bsky, 19 points, 1 comments]
• doi.org/10.1126/scie... (41/41) [bsky, 0 points, 0 comments]

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