Fake news¶
Fake news refers to news articles or claims that are intentionally and verifiably false, fabricated to mislead readers. The term is distinct from satire (which is labeled as fiction), unintentional errors, opinion/slant, and conspiracy theories (which are difficult to definitively verify as false).
The focus of research on fake news is understanding its production (who makes it, why, and how), distribution (how it spreads, especially via social media), consumption (who is exposed and who believes), and effects (on political beliefs, voting, and public discourse).
Key observations¶
Partisan asymmetry in production:
In the 2016 U.S. election, pro-Trump fake news articles vastly outnumbered pro-Clinton articles (115 vs. 41), and pro-Trump content received roughly 3x more engagement on Facebook Allcott & Gentzkov (2017)
Fake news reaches substantial audiences:
The average American adult was exposed to approximately 1–2 fake news articles during the 2016 election period, though exposure is heavily concentrated among users most active on social media and ideologically polarized networks Allcott & Gentzkov (2017)
Economic incentives shape production:
Fake news supply is driven by low barriers to entry on social media, advertising revenue, and partisan motivation. Producers often have no long-term reputation concerns, distinguishing them from traditional news outlets Allcott & Gentzkov (2017)
Related concepts¶
- Misinformation spread and diffusion — how fake news propagates through networks
- Political bias — partisan nature of fake news production and consumption
- Content-based detection — methods to identify false claims
- User profiles — susceptibility and sharing of fake news by user type
- Credibility assessment — assessing the trustworthiness of sources and claims
Key papers and datasets in this wiki¶
- The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans — Comprehensive typology of false information ecosystem; surveys 200+ papers on actors, types, motives, and detection approaches
- Mohseni & Ragan (2018) — Combating Fake News with Interpretable News Feed Algorithms — Review of fake news detection methods across creation, distribution, and consumption stages; argues for interpretable news feed algorithms as prevention mechanism
- Combating Misinformation in the Age of LLMs: Opportunities and Challenges — Survey covering both opportunities for detection and challenges from LLM-generated fake news, rumors, clickbait, and propaganda
- Gruppi, Horne & Adalı (2022) — NELA-GT-2022 — Large-scale news dataset with 1.78M articles from 361 outlets spanning 2022; includes source-level veracity labels from Media Bias/Fact Check and embedded tweet data; benchmark resource for detection and media dynamics research
- Mihailidis & Viotty (2017) — Spreadable Spectacle in Digital Culture — Critical analysis of Pizzagate and how false narratives spread through digital networks and homophilous communities during the 2016 election. Examines how spectacle, memes, and mainstream media coverage legitimate false claims.
- Tandoc, Lim, & Ling (2017) — Defining "Fake News": A typology of scholarly definitions — foundational typology of fake news definitions based on facticity and intent; identifies six types (satire, parody, fabrication, manipulation, advertising, propaganda)
- Wardle & Derakhshan (2017) — Information Disorder: Toward an Interdisciplinary Framework for Research and Policy Making — foundational framework distinguishing mis-, dis-, and mal-information; arguments for terminology precision and agent-message-interpreter analysis
- Allcott & Gentzkov (2017) — Social Media and Fake News in the 2016 Election — empirical measurement, partisan patterns, and economic framework
- Allen et al. (2020) — Evaluating the fake news problem at the scale of the information ecosystem — ecosystem-scale measurement showing fake news comprises 0.15% of daily media diet; TV dominates news consumption by 5:1 over online
- Tsfati et al. (2020) — Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis — examines how mainstream outlets amplify fake news despite limited reach of native fake news sites; identifies four mechanisms driving coverage (journalistic duty, news values, psychology, infrastructure) and mechanisms explaining why corrections backfire
- Effron & Raj (2020) — Misinformation and Morality: Encountering Fake-News Headlines Makes Them Seem Less Unethical to Publish and Share — experimental evidence that repeated exposure to fake-news headlines reduces moral condemnation of spreading them; reveals moral desensitization as a driver of sharing behavior independent of belief
- Zhou & Zafarani (2020) — A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities — comprehensive survey of detection methods and theory
- Vosoughi, Roy, & Aral (2018) — The Spread of True and False News Online
Open challenges¶
- How do different platforms and algorithms shape fake news production and distribution?
- What are the long-term effects of exposure to fake news on political beliefs and behavior?
- How effective are interventions (labels, friction, counter-narratives) in reducing fake news harm?
- How do fact-checking sites' editorial choices bias the observable fake news landscape?
- What role do emotional and surprise-driven mechanisms play in fake news appeal and spread?