Misinformation

False or misleading information—including its creation, spread, belief, and persistence. This topic covers psychological mechanisms, social dynamics, and cognitive factors underlying misinformation susceptibility and resistance.

Key papers and articles

• Tang et al. (2023) — Survey of detecting LLM-generated text, motivated by concerns about misinformation production and misuse; covers detection methods (black-box and white-box), benchmarks, and challenges
• Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models' Alignment — Survey of LLM trustworthiness dimensions including resistance to being misused to generate misinformation, propaganda, and false information; measures hallucination, misinformation generation propensity, and safety guardrails across multiple aligned and unaligned models
• Shu et al. (2020) — Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements — comprehensive survey of misinformation research emphasizing cognitive biases (naive realism, confirmation bias), social dynamics (homophily, echo chambers), and weak social supervision approaches that leverage user behavior and network structure for detection without extensive annotation.
• Memon & Carley (2020) — Characterizing COVID-19 Misinformation Communities — Twitter dataset (CMU-MisCOV19) with manual annotations across 17 misinformation and information categories; characterizes misinformed communities as denser, more bot-coordinated (19% bots), and less narrative-driven than informed communities; suggests one-size-fits-all messaging approaches may be ineffective for organized disinformation
• Pennycook et al. (2021) — Shifting attention to accuracy can reduce misinformation online — Nature paper showing that subtle reminders to focus on accuracy increase sharing of accurate news across six experiments and a Twitter field experiment; demonstrates limited attention, not confusion, as primary mechanism driving misinformation sharing
• Cinelli et al. (2020) — The COVID-19 Social Media Infodemic — Multi-platform comparative analysis of COVID-19 misinformation diffusion across Twitter, Instagram, YouTube, Reddit, and Gab; models information spreading with epidemic models and characterizes platform-specific rumor amplification
• Mihailidis & Viotty (2017) — Spreadable Spectacle in Digital Culture — Analysis of how spectacle (media constructs that create excitement, drama, and polarization) spreads through digital networks. Uses Pizzagate conspiracy theory to examine technical, social, and structural factors enabling rapid misinformation diffusion in homophilous communities.
• Guess et al. (2020) — A digital media literacy intervention increases discernment between mainstream and false news in the United States and India — RCT testing simple media literacy "tips" as a scalable intervention; shows 26.5% improvement in discernment in US, effects decay over time but persist weeks later
• Walter et al. (2020) — Fact-Checking: A Meta-Analysis of What Works and for Whom — meta-analysis of 30 studies on fact-checking as a misinformation correction strategy; identifies motivated reasoning, ideology, and message design as key moderators of effectiveness.
• Stella, Ferrara & De Domenico (2018) — Bots increase exposure to negative and inflammatory content in online social systems — demonstrates bot-driven amplification of inflammatory misinformation during political events; shows bots reinforce and amplify group-specific narratives (violent, negative content for Independentists vs. neutral for Constitutionalists); evidence of coordinated inauthentic behavior intensifying human-to-human misinformation spread
• Lewandowsky et al. (2012) — Misinformation and its correction: Continued influence and successful debiasing — foundational review synthesizing literature on why false beliefs persist despite corrections; examines cognitive mechanisms (coherence, credibility, source confusion, fluency) and evidence-based debiasing strategies
• Giglietto et al. (2020) — It takes a village to manipulate the media — empirical evidence that coordinated networks amplify problematic (false/misleading) content 1.79–2.22× more frequently than uncoordinated actors; methods for detecting coordinated spreading behavior
• Mosleh et al. (2021) — Cognitive reflection correlates with behavior on Twitter — field evidence linking cognitive reflection to discerning social media behavior and news source selection
• Ecker et al. (2022) — The psychological drivers of misinformation belief and its resistance to correction — comprehensive review of cognitive, social, and affective mechanisms in false belief formation, continued influence effects, and evidence-based interventions (prebunking and debunking)
• Effron & Raj (2020) — Misinformation and Morality: Encountering Fake-News Headlines Makes Them Seem Less Unethical to Publish and Share — shows that repeated exposure to fake-news headlines reduces moral condemnation of sharing them independent of belief; reveals moral desensitization pathway separate from epistemic effects
• Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning — Pennycook & Rand on cognitive factors in fake news belief
• Wilson & Wiysonge (2020) — Social media and vaccine hesitancy — cross-national analysis showing social media organization for offline action predicts vaccine safety skepticism; demonstrates behavioral downstream effects of misinformation (declining vaccination rates)