David G. Rand

David G. Rand is a researcher at MIT working on behavioral science, misinformation, and the psychology of reasoning. His research examines how people form beliefs, make decisions in social contexts, and respond to misinformation, with a focus on scalable interventions for combating false information online.

Sources in this wiki

• Bhardwaj et al. (2023) — Examining accuracy-prompt efficacy in combination with using colored borders to differentiate news and social content online — Tests accuracy prompts and visual borders in realistic newsfeeds with mixed news and social content; finds colored borders don't enhance accuracy-prompt effectiveness.
• Jahanbakhsh et al. (2021) — Exploring Lightweight Interventions at Posting Time to Reduce the Sharing of Misinformation on Social Media — Tests accuracy assessment and reasoning nudges; finds both reduce false headline sharing by ~17%, with greater effect for reasoning nudges
• Pennycook et al. (2024) — Technique-based inoculation and accuracy prompts must be combined to increase truth discernment online — Five experiments show psychological inoculation and accuracy prompts have synergistic effects; combined intervention improves truth discrimination by 7–10%
• Lin et al. (2026) — Reducing misinformation sharing at scale using digital accuracy prompt ads
• Accuracy prompts are a replicable and generalizable approach for reducing the spread of misinformation (2022)
• Nudging social media sharing towards accuracy (2022)
• Xu et al. (2026) — Personalized AI Dialogues and Parents' HPV Vaccine Intent: A Randomized Trial — Demonstrates that a single dialogue with GPT-4 Turbo significantly increases parental HPV vaccination intentions; the AI's responses were highly accurate (99.6% of 15,559 claims fact-checked correct)
• Czarnek et al. (2026) — Addressing climate change skepticism and inaction using human-AI dialogues — Demonstrates that personalized LLM-driven dialogues addressing individuals' specific climate concerns substantially reduce skepticism and increase willingness to take climate action, outperforming consensus messaging
• Costello et al. (2026) — Reducing belief in conspiracy theories as they unfold using large language models — Demonstrates that LLM-based dialogue effectively reduces conspiracy belief about emerging events in real-time; identifies seven persuasion strategies employed by the model and provides evidence for downstream effects on related conspiracy beliefs
• Hackenburg et al. (2025) — The Levers of Political Persuasion with Conversational AI — Large-scale study (76,977 participants, 19 LLMs, 707 political issues) of conversational AI's persuasive capacity; shows conversational interaction substantially more persuasive than static messages (+2.94pp–+4.1pp), larger models more persuasive, and post-training increases persuasiveness by 51%; critically, persuasive models systematically generate less accurate claims.
• Boissin et al. (2026) — Dialogues with Large Language Models Reduce Conspiracy Beliefs Even When the AI Is Perceived as Human — Preregistered experiment (N=955) testing whether messenger identity (AI vs. human expert) affects debunking effectiveness; finds effect robust across messenger conditions, supporting the hypothesis that evidence quality, not source characteristics, drives belief reduction
• Costello, Pennycook & Rand (2026) — Just the Facts: How Dialogues with AI Reduce Conspiracy Beliefs — Two large experiments (N=2,193) showing that AI-mediated dialogue durably reduces conspiracy beliefs through factual counterargument; facts and evidence are load-bearing, while rapport and narrative contribute negligibly; demonstrates conspiracy beliefs are updatable contrary to decades of prior folklore.
• Lin, Pennycook & Rand (2023) — Thinking more or thinking differently? Using drift-diffusion modeling to illuminate why accuracy prompts decrease misinformation sharing — Tests whether accuracy prompts work by increasing deliberation or by shifting attention to accuracy; finds evidence for attention mechanism, not increased deliberation
• Guay, Berinsky, Pennycook & Rand (2023) — How to Think About Whether Misinformation Interventions Work — Methodological framework for evaluating interventions using discernment (true − false content discrimination) as the primary outcome
• Lin et al. (2023). High level of concordance across different news domain quality rating sets. — Comprehensive domain credibility ratings dataset combining six expert sources; PC1 aggregate rating captures ~68% of variance
• Mosleh et al. (2024). Differences in misinformation sharing can lead to politically asymmetric sanctions — Nature paper showing political asymmetries in platform enforcement reflect asymmetric misinformation-sharing patterns
• Field experiments on social media (2021) — Methodological review with Mosleh and Pennycook surveying field experimental approaches to studying behavior on social media, covering hybrid lab-field designs and pure field experiments; discusses ethical considerations and practical guidance.
• 2021-pennycook-psychology-fake-news — The Psychology of Fake News (with Pennycook)
• 2020-pennycook-covid19-accuracy-nudge — Fighting COVID-19 Misinformation on Social Media (with Pennycook)
• 2021-pennycook-shifting-attention-accuracy — Shifting attention to accuracy can reduce misinformation online (with Pennycook)
• 2019-pennycook-crowdsourced-news-source-quality — Fighting misinformation on social media using crowdsourced judgments of news source quality (with Pennycook)
• 2020-pennycook-implied-truth-effect — The Implied Truth Effect (with Pennycook)
• 2020-van-bavel-social-behavioral-science-covid-19 — The Social Science of COVID-19 (contributor)

Topics

• misinformation, fake-news, reasoning, behavioral-interventions

Notes

Frequent collaborator with Gordon Pennycook on misinformation interventions, particularly accuracy-salience approaches and the belief-sharing disconnect on social media. Co-leads the Pentland Misinformation Lab research group.