Misinformation spread and diffusion

The study of how false, misleading, or inaccurate claims propagate through populations, social networks, and online platforms. This includes the mechanisms, speed, and breadth of misinformation distribution, as well as factors that explain why some claims spread more readily than others.

Key observations

False news spreads faster and farther: Across multiple platforms and content domains, false claims reach more people, deeper cascade depths, and higher virality than true claims The Spread of True and False News Online

Humans, not bots, drive false news: Contrary to early concerns, automated accounts (bots) accelerate both true and false news equally. Human sharing behavior is the primary driver of the misinformation spread disparity The Spread of True and False News Online

Novelty is a powerful signal: False claims are perceived as more novel and surprising, and novelty drives sharing behavior. This mechanism explains—from an information-theoretic perspective—why people preferentially spread surprising information The Spread of True and False News Online

Context and emotion matter: Political misinformation, urban legends, and emotionally charged content spread fastest. Natural disasters, terrorism, and financial claims trigger rapid but different diffusion patterns

Related concepts

• Propagation-based detection — using diffusion patterns as detection signals
• Social-context-based detection — role of user networks and social influence
• User profiles — susceptibility and share likelihood varies by user type
• COVID-19 misinformation — domain-specific case study

Key papers in this wiki

• Kim et al. (2017) — Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation — formulates the problem of scheduling crowd-sourced fact-checking as a stochastic optimal control problem; develops CURB algorithm to minimize spread of misinformation given uncertain exposure dynamics and limited fact-checking resources.
• Treen, Williams & O'Neill (2020) — Online misinformation about climate change — examines mechanisms of misinformation diffusion including epidemic/contagion models, homophily, echo chambers, and algorithmic bias; analyzes networks of actors producing and amplifying false claims on social media; synthesizes psychological factors contributing to differential susceptibility.
• Grinberg et al. (2019) — Fake news on Twitter during the 2016 U.S. presidential election — individual-level exposure analysis on Twitter during 2016; demonstrates extreme concentration of consumption (1% of users see 80% of fake news); shows mainstream media still dominant in most users' feeds; political congruency drives sharing more than veracity.
• Guess, Nagler & Tucker (2019) — Less than you think: Prevalence and predictors of fake news dissemination on Facebook — Facebook-based study; finds sharing was rare but concentrated among older users; 65+ users 7× more likely to share than youngest group; challenges ideology-driven explanations in favor of age/literacy mechanisms.
• Acerbi (2019) — Cognitive attraction and online misinformation — proposes that misinformation spreads due to psychological appeal rather than media-system inefficiency; shows 86% of hoax articles contain threat-related content, negative framing, social information, or other cognitive-preference elements; reframes misinformation as "high-quality" when measured by appeal, not truthfulness
• Information Disorder: Toward an Interdisciplinary Framework for Research and Policy Making (2017) — conceptual framework for agents, messages, and interpreters across creation-production-distribution lifecycle
• Social Media and Fake News in the 2016 Election (2017) — empirical measurement of fake news exposure, partisan asymmetry, and economic supply/demand framework
• The Spread of True and False News Online (2017) — foundational empirical work on true vs. false news diffusion
• A Survey of Fake News (2020) — theoretical frameworks for understanding spread mechanisms
• Network-based Fake News Detection (2019)

Open challenges

• How do different platforms (Twitter, Facebook, TikTok, messaging apps) shape misinformation spread differently?
• What are the effects of platform interventions (labels, friction, prompting) on cascade dynamics?
• How does information quality decay or change as misinformation spreads across generations?
• What role do echo chambers, filter bubbles, and algorithmic amplification play post-2017?