About

David Rand is a professor affiliated with the Department of Psychology at Cornell University. He holds positions as a Professor of Marketing and Management Communications at the S.C. Johnson Graduate School of Management and as a Professor of Information Science at the A.S. Bowers College of Computing and Information Science. His academic work spans multiple departments, reflecting a multidisciplinary approach to understanding human cognition, decision-making, and social behavior. The information provided indicates his involvement in research related to psychology, social and personality studies, and the intersection of technology and human behavior, contributing to the university's diverse academic community.

Research topics

• Political Science
• Psychology
• Computer Science
• Sociology
• Social psychology
• Computer Security
• Medicine
• Social Science
• Law
• Public relations
• Advertising
• Internet privacy
• Psychiatry
• World Wide Web
• Cognitive psychology
• Engineering
• Epistemology
• Artificial Intelligence
• Business
• Clinical psychology
• Art history
• Mathematics
• Marketing
• Art

Selected publications

• How malicious AI swarms can threaten democracy

  Science · 2026-01-22 · 10 citations

  articleOpen access

  The fusion of agentic AI and LLMs marks a new frontier in information warfare.

  PublisherOA PDFDOI

• Divergent patterns of engagement with partisan and low-quality news across seven social media platforms

  2025-09-16

  preprintOpen accessSenior author

  In recent years, social media has become increasingly fragmented, as platforms evolve and new alternatives emerge. Yet most research studies a single platform—typically Twitter/X, or occasionally Facebook—leaving little known about the broader social media landscape. Here we shed new light on patterns of cross-platform variation in the high-stakes context of news sharing. We examine the relationship between user engagement and news domains’ political orientation and quality across seven platforms: Twitter/X, BlueSky, TruthSocial, Gab, GETTR, Mastodon, and LinkedIn. Using an exhaustive sampling strategy, we analyze all (over 10 million) posts containing links to news domains shared on these platforms during January 2024. We find that the news shared on platforms with more conservative user bases is significantly lower quality on average. Turning to patterns of engagement, we find—contrary to hypotheses of a consistent “right wing advantage” on social media—that the relationship between political lean and engagement is strongly heterogeneous across platforms. Conservative new posts receive more engagement on platforms where most content is conservative, and vice versa for liberal news posts, consistent with an “echo platform” perspective. In contrast, the relationship between news quality and engagement is strikingly consistent: across all platforms examined, lower-quality news posts received higher average engagement even though higher quality news is substantially more prevalent and garners far more total engagement across posts. This pattern holds despite accounting for poster-level variation, and is observed even in the absence of ranking algorithms, suggesting user preferences – not algorithmic – bias may underlie the underperformance of higher-quality news.

  PublisherOA PDFDOI

• Addressing misperceptions takes more than combating fake news

  Trends in Cognitive Sciences · 2025-08-04 · 3 citations

  reviewSenior author
  PublisherDOI

• Replicability and generalizability of the repeated exposure effect on moral condemnation of fake news

  Nature Communications · 2025-08-05 · 1 citations

  articleOpen accessSenior author

  Repeated exposure to misinformation reduces moral condemnation of those falsehoods, as shown by Effron & Raj (2020)1—and moral condemnation may play an important role in stopping the spread of online misinformation. In this registered report, we conceptually replicate previous findings on the effect of repetition and moral condemnation and investigate the generalizability of the findings, using an updated and larger set of false headlines. We also investigate whether asking for accuracy evaluations of the headlines, a type of accuracy prompt that is standard in repeated exposure tasks, alters the effect of repetition on moral condemnation, as inattention to the veracity of headlines may decrease outrage and thus moral condemnation. We find a clear conceptual replication of the negative effect of repetition on moral condemnation, and insufficient evidence for a relationship between accuracy prompts and the effect of repetition. Repeated exposure to misinformation reduces moral condemnation of sharing those falsehoods online. Here, the authors show that this finding replicates and generalizes to new settings and headlines.

  PublisherOA PDFDOI

• Conversations with a large language model improve attitudes toward Muslims and Islam without harming attitudes toward Jews or Christians

  2025-10-31

  articleOpen access1st authorCorresponding

  Can a brief conversation with a large language model (LLM) measurably improve attitudes toward a stigmatized religious minority? We investigate this question in the context of Americans’ attitudes towards Muslims and Islam. We also ask whether doing so worsens attitudes towards Jews and Judaism, given the current high-salience conflict between Muslims and Jews in the Middle East. To answer these questions, N=604 participants had a three-round dialogue with an LLM that either (i) corrected participants’ misconceptions about Islam and Muslims (treatment) or (ii) discussed sports (control). As expected, we find that the treatment significantly improved attitudes towards Islam and Muslims relative to the control. Furthermore, we do not find that improving attitudes towards Islam and Muslims comes at the cost of worsening attitudes towards Jews or Christians. On the contrary, we find some evidence that the treatment also improves attitudes towards Jews, Judaism, and Christianity. Thus, customized LLM chatbots may help address religious prejudice.

  PublisherDOI

• The Unintended Consequences of Labeling AI-Generated Media Online

  2025-12-18

  articleOpen accessSenior author

  Media platforms have recently introduced initiatives to label AI-generated media, aiming to increase transparency about content creation. Yet such efforts may carry unintended consequences. AI-generated media often accompany informational content that can vary in veracity. However, labeling may confound perceptions of the media's authenticity and the content's veracity, reducing belief in true information. Moreover, since it isn’t feasible to label all AI-generated media, partial labeling may lead people to assume that the absence of a label implies authenticity and/or veracity. We test for these labeling and implied effects in two survey experiments (N = 11,044), where respondents evaluated political news posts. Labeling decreased perceptions of the authenticity of AI-generated images but also lowered belief in and willingness to share posts—even when the associated claims were true. Furthermore, exposure to partial labeling increased the perceived authenticity of unlabeled content. These results highlight the need for carefully designed labeling practices online.

  PublisherDOI

• Dialogues with Large Language Models reduce conspiracy beliefs even when the AI is perceived as human

  2025-09-11

  preprintOpen access

  Although conspiracy beliefs are often viewed as resistant to correction, recent evidence shows that personalized, fact-based dialogues with artificial intelligence (AI) can reduce them. Is this effect driven by the debunking facts and evidence, or does it rely on the messenger being an AI model? In other words, would the same message be equally effective if delivered by a human? To answer this question, we conducted a preregistered experiment (N = 955) in which participants reported either a conspiracy belief or a non-conspiratorial but epistemically unwarranted belief, and interacted with an AI model that argued against that belief using facts and evidence. We randomized whether the debunking AI model was characterized as an AI tool or a human expert and whether the model used human-like conversational tone. The conversations significantly reduced participants’ confidence in both conspiracies and epistemically unwarranted beliefs, with no significant differences across conditions. Thus, AI persuasion is not reliant on the messenger being an AI model: it succeeds by generating compelling messages.

  PublisherDOI

• Increasing the effectiveness of charitable giving using human-AI dialogues

  2025-09-02

  articleOpen accessSenior author

  Charitable donations frequently fail to maximize cost-effectiveness (the amount of good a donation does per dollar). This failure is often attributed to charitable motivations being affective and thus insensitive to evidence-based arguments. We challenge this perspective, hypothesizing that evidence can substantially increase effective giving—if that evidence is sufficiently compelling. We test this prediction in a pre-registered experiment (N = 1,949 Americans) by leveraging the ability of artificial intelligence large language models (LLMs) to engage in evidence-based back-and-forth dialogues. Participants allocated $1 between their favorite charity and a highly effective charity (the Against Malaria Foundation), before and after a conversation with an LLM advocating for the effective charity, a static LLM-generated persuasive message, or a control conversation. The LLM conversationsignificantly increased effective donations (45.9%), a significantly larger increase than the static message or control, as well as shifting moral attitudes. Effective giving can be meaningfully increased through evidence-based dialogues.

  PublisherOA PDFDOI

• @Grok Is This True? LLM-Powered Fact-Checking on Social Media

  2025-12-03 · 1 citations

  articleOpen accessSenior author

  Large language models (LLMs) are increasingly embedded directly into social media platforms, enabling users to request real-time fact-checks of online content. Using an exhaustive dataset of 1,671,841 English-language fact-checking requests made to Grok and Perplexity on X between February and September 2025, we provide the first large-scale empirical analysis of how LLM-based fact-checking operates in the wild. Fact-checking requests comprise 7.6% of all interactions with the LLM bots, and focus primarily on politics, economics, and current events. We document clear partisan asymmetries in usage. Users requesting fact-checks from Grok are much more likely to be Republican than Democratic, while the opposite is true for fact-check requests from Perplexity -- indicating emerging polarization in attitudes toward specific AI models. At the same time, both Democrats and Republicans are more likely to request fact-checks on posts authored by Republicans, and - consistent with prior work using professional fact-checkers and crowd judgments - posts from Republican-leaning accounts are more likely to be rated as inaccurate by both LLMs. Across posts rated by both LLM bots, evaluations from Grok and Perplexity agree 52.6% of the time and strongly disagree (one party rates a claim as true and the other as false) 13.6% of the time. For a sample of 100 fact-checked posts, 54.5% of Grok bot ratings and 57.7% of Perplexity bot ratings agreed with ratings of human fact-checkers, which is significantly lower than the inter-fact-checker agreement rate of 64.0%; but API-access versions of Grok had higher agreement with fact-checkers and did not significantly differ from inter-fact-checker agreement. Finally, in a preregistered survey experiment with 1,592 U.S. participants, exposure to LLM fact-checks meaningfully shifts belief accuracy, with effect sizes comparable to those observed in studies of professional fact-checking. However, responses to Grok fact-checks are polarized by partisanship when model identity is disclosed, whereas responses to Perplexity are not. Together, these findings show that LLM-based fact-checking is rapidly scaling, is generally informative although far from perfect, while also becoming entangled with polarization and partisanship. Our work highlights both the promise and the risks of integrating AI fact-checking into online public discourse.

  PublisherDOI

• Dialogues with large language models reduce conspiracy beliefs even when the AI is perceived as human

  PNAS Nexus · 2025-10-14 · 8 citations

  articleOpen access

  Abstract Although conspiracy beliefs are often viewed as resistant to correction, recent evidence shows that personalized, fact-based dialogues with a large language model (LLM) can reduce them. Is this effect driven by the debunking facts and evidence, or does it rely on the messenger being an AI? In other words, would the same message be equally effective if delivered by a human? To answer this question, we conducted a preregistered experiment (N = 955) in which participants reported either a conspiracy belief or a nonconspiratorial but epistemically unwarranted belief and interacted with a LLM that argued against that belief using facts and evidence. We randomized whether the debunking LLM was characterized as an AI tool or a human expert and whether the model used human-like conversational tone. The conversations significantly reduced participants' confidence in both conspiracies and epistemically unwarranted beliefs, with no significant differences across conditions. Thus, AI persuasion is not reliant on the messenger being an AI model: it succeeds by generating compelling messages.

  PublisherOA PDFDOI

Frequent coauthors

• Gordon Pennycook

  University of Regina

  252 shared

• Antonio A. Arechar

  Centro de Investigación y Docencia Económicas

  116 shared

• Mohsen Mosleh

  Massachusetts Institute of Technology

  111 shared

• Martin A. Nowak

  Harvard University

  64 shared

• Cameron Martel

  53 shared

• Hause Lin

  Massachusetts Institute of Technology

  45 shared

• Adam J. Berinsky

  Massachusetts Institute of Technology

  43 shared

• Ziv Epstein

  Stanford University

  37 shared