About

William Brady is an Associate Professor of Management and Organizations at the Kellogg School of Management, Northwestern University. His research examines the dynamics of emotion at the social network level and their consequences for group behavior. His recent work studies how human psychology and AI-mediated social contexts interact to shape emotions and intergroup attitudes. Combining tools of behavioral science and computational social science, his research aims to develop person-centered and design-centered interventions to improve digital social interactions. Professor Brady's research has been published in leading journals such as Science, PNAS, Nature Human Behaviour, Science Advances, and Perspectives on Psychological Science. His work has also been featured in popular press outlets including The New York Times, BBC, Wired, and The Wall Street Journal. In recognition of his contributions, he has received awards such as the Association for Psychological Science Rising Star Award and the SAGE Emerging Scholar award. He earned his BA in Psychology and Philosophy, with distinction, from UNC-Chapel Hill, his Ph.D. in Social Psychology at New York University, and completed a postdoctoral fellowship from the National Science Foundation at Yale University.

Research topics

• Political Science
• Computer Science
• Psychology
• Social psychology
• Economics
• Medicine
• Social Science
• Sociology
• Data science
• Econometrics
• Cognitive psychology
• Business
• Mathematics
• Development economics
• Applied psychology
• World Wide Web
• Marketing
• Statistics
• Demographic economics
• Demography
• Law

Selected publications

• Rising Moralization in Social Media Discourse

  PsyArXiv (OSF Preprints) · 2026-02-07

  preprintOpen access

  In the attention economy of social media, moralized commentary spreads widely, 15 attracts engagement, and is amplified by algorithms. Yet little is known about temporal trends in moralization—specifically, whether moralization has steadily increased, a trend that could deepen divisions and fuel polarization. Using natural language processing, we analyzed 9.7M Twitter/X posts and found a sharp rise in moralized language from 2013-2021. This trend generalized to Reddit (2.1B comments) and outpaced changes in two traditional media corpora 20 (4.9M and 115K texts). Two processes explained this moralizing shift: (1) within-user increases in moral language over time, and (2) selection effects, whereby highly moralized users became more active while less moralized users disengaged. These findings reveal how user dynamics can create highly moralized discourse; understanding this process is crucial for fostering healthier digital ecosystems.

  Publisher

• Characterizing In-Flight Medical Emergencies on Commercial Airline Flights

  JAMA Network Open · 2025-09-29

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Engagement-based algorithms disrupt human social norm learning

  2025-02-13 · 1 citations

  preprintOpen access1st authorCorresponding

  As we increasingly learn from others in online spaces regulated by engagement-based feed-ranking algorithms, it is important to understand the impact that these algorithms have on our social learning. In three pre-registered studies (N = 6107), we isolated the effects of feed-ranking algorithms on social norm learning by training algorithms on passive and active engagement in a simulated social media environment. We found that engagement-based algorithms systematically amplified ingroup-aligned, moral and emotional (IME) political content, leading IME content to become overrepresented in feeds. The overrepresentation of IME content in feeds caused participants to overperceive norms of posting IME content, which mediated user intentions to post more IME content. A bridging algorithm successfully reduced IME content in feeds, but also led to underperception of some IME content, suggesting that bridging-based algorithms do not straightforwardly promote more accurate social learning. Our findings shed light on algorithm-mediated social learning in the digital age, demonstrating that specific human learning biases toward IME content are amplified by engagement-based algorithms in ways that disrupt social norm perception. Our findings highlight a central challenge for engagement-based algorithms: how to promote content in ways that enable users to accurately infer others’ preferences.

  PublisherDOI

• Estimating the effect size of moral contagion in online networks: A pre-registered replication and meta-analysis

  2025-04-02

  preprintOpen access1st authorCorresponding

  Over 5 billion people now use social media platforms. As our social lives become more entangled than ever before with online social networks, it is important to understand the dynamics of online information diffusion. This is particularly true for the political domain, as political elites, disinformation profiteers, and social activists all utilize social media to gain influence by spreading information. Recent work found that emotional expressions related to the domain of morality (moral emotion expression) are associated with increased diffusion of political messages--a phenomenon we called ‘moral contagion’. Here, we perform a large, pre-registered direct replication (N = 849,266) of Brady et al. (2017) using the dictionary methods employed in the original paper, as well as new large-language models. We also perform a meta-analysis of all available data testing moral contagion (5 different labs, 27 studies, N = 4,821,006). The estimate of moral contagion in the available population of studies is positive and significant (IRR = 1.12, 95% CI = [1.06, 1.19]), such that for each additional moral-emotional word in a social media post, the expected number of shares was 15% greater. The mean effect size of the large, pre-registered replication (IRR = 1.15) better estimated the effect size of the available population of studies than the original study (IRR = 1.20). Contrary to prior work, we find that the moral contagion model substantially outperforms non-sense models of diffusion (‘XYZ contagion model’). Moral contagion was also conceptually replicated when moral-emotional content was measured using state-of-the-art methods in natural language processing (large language models). These findings reveal that the moral contagion effect is highly robust using multiple datasets and methods.

  PublisherDOI

• Social identity shapes antecedents and functional outcomes of moral emotion expression.

  Journal of Experimental Psychology General · 2025-04-07 · 7 citations

  articleOpen access1st authorCorresponding

  = 2,498), we find robust evidence that the inclusion of moral-emotional expressions in political messages increases intentions to share the messages on social media. Moreover, individual differences in the strength of partisan identification and ideological extremity are robust predictors of sharing messages with moral-emotional expressions, even when accounting for attitude strength. However, we only found mixed evidence that brief manipulations of identity salience increased sharing. In terms of functional outcomes, when partisans choose to share messages with moral-emotional language, people perceive them as more strongly identified among their partisan ingroup but less open minded and less worthy of conversation with outgroup members. These experiments highlight the causal role of moral-emotional expression in online sharing intentions and how such expressions in online networks can serve ingroup reputation functions while hindering discourse between political groups. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

  PublisherDOI

• Estimating the effect size of moral contagion in online networks: A pre-registered replication and meta-analysis

  PNAS Nexus · 2025-10-14 · 1 citations

  articleOpen access1st authorCorresponding

  Abstract Over 5 billion people now use social media platforms. As our social lives become increasingly entangled with online social networks, it is important to understand the dynamics of online information diffusion. This is particularly true for the political domain, as political elites, disinformation profiteers, and social activists all use social media to gain influence by spreading information. Recent work found that emotional expressions related to morality (moral-emotion expression) are associated with increased diffusion of political messages—a phenomenon we called “moral contagion.” Here, we perform a large, pre-registered direct replication (N = 849,266) of Brady et al. using the dictionary methods from the original paper, as well as new large-language models. We also conduct a meta-analysis of all available data testing moral contagion (5 labs, 27 studies, N = 4,821,006). The estimate of moral contagion in the available population is positive and significant (IRR = 1.13, 95% CI = [1.06, 1.20]), such that for each additional moral–emotional word in a post, the expected number of shares was 13% greater. The mean effect size of the pre-registered replication (IRR = 1.17) better estimated the population effect than the original study (IRR = 1.20). Contrary to prior work, we find that the moral contagion model substantially outperforms nonsense models of diffusion (“XYZ contagion model”). Moral contagion was also conceptually replicated when moral–emotional content was measured using state-of-the-art natural language processing methods. These findings reveal that the moral contagion effect is highly robust across datasets and methods.

  PublisherOA PDFDOI

• Affective And Cognitive Underpinnings of Moral Condemnation When News of Transgressions Goes Viral

  2025-07-21

  preprintOpen accessSenior author

  When news of a transgression goes viral, people hear about it repeatedly from different news sources and individuals. How does this repeated exposure affect moral judgments of the transgression? We test a new theoretical model proposing that moral condemnation is influenced by competing affective and cognitive processes. Repeated exposure to the same information about a transgression dampens people’s emotional responses, which can reduce moral condemnation (an affective-desensitization process). However, repeated exposure from multiple sources also signals that the transgression is receiving widespread negative attention, which can increase moral condemnation (a cognitive infamy-inference process). These processes’ net effect will depend on how strongly repetition dampens affect vs. signals infamy. Five preregistered experiments (N = 3,939) test our model. Participants rated corporate transgressions to which they had or had not been repeatedly exposed from three sources (news outlets or individuals). Experiments 1 and 2 measured affective reactions, infamy inferences, and moral judgments, finding mediational support for our model. In Experiment 2, and two supplemental experiments, repetition reduced moral condemnation, suggesting that affective desensitization was the dominant process. Experiment 3 was designed to strengthen the infamy process by highlighting over a million negative reactions to each repeatedly seen transgression; consistent with our model, infamy no longer reduced moral condemnation but continued to dull affective reactions, suggesting that affective desensitization and infamy-inference processes offset one another. By documenting these countervailing processes, our research deepens understanding of when, why, and how viral transgressions may impact public opinion and moral outrage.

  PublisherDOI

• Differentiation Drives the Erosion of Positivity on Social Media

  2025-10-02

  articleOpen access

  Most people believe that social media discourse is negative and divisive. Here we show how this negativity can evolve even when users are not motivated to be negative. We propose that social media users seek to differentiate themselves from other users, and it is easier to differentiate oneself through negativity than positivity because negative information is more heterogeneous and counter-normative than positive information. This makes users increasingly likely to post negative comments as a conversation unfolds and it becomes more challenging to make unique contributions. Analyzing 2.05 billion comments from 2,150 Reddit communities shows that comments become more negative over time, both within threads and community histories. This trend towards negativity is mediated by the semantic uniqueness of comments, suggesting that it arises from users differentiating themselves. This trend is strongest when initial dialogue is positive, making negative comments highly counter-normative. We replicate these patterns in a multigenerational experiment simulating social media dialogue (n = 4,000). Participants become more negative over time, but only when incentivized to be unique, and especially when dialogue begins positively. These findings suggest that the structure of social media platforms interacts with human motivation to foster a drift towards negativity over time in online discourse.

  PublisherOA PDFDOI

• Disagreements About Threats to Electoral Integrity: Beliefs About the Severity and Frequency of Fraudulent, Uncounted, and Forgone Votes in the 2020 and 2024 Elections

  Political Behavior · 2025-07-30 · 2 citations

  article
  PublisherDOI

• Rising Moralization in Social Media Discourse

  2025-07-22

  preprintOpen access

  In the attention economy of social media, moralized commentary spreads widely,15 attracts engagement, and is amplified by algorithms. Yet little is known about temporal trends inmoralization—specifically, whether moralization has steadily increased, a trend that coulddeepen divisions and fuel polarization. Using natural language processing, we analyzed 9.7MTwitter/X posts and found a sharp rise in moralized language from 2013-2021. This trendgeneralized to Reddit (2.1B comments) and outpaced changes in two traditional media corpora20 (4.9M and 115K texts). Two processes explained this moralizing shift: (1) within-user increasesin moral language over time, and (2) selection effects, whereby highly moralized users becamemore active while less moralized users disengaged. These findings reveal how user dynamics cancreate highly moralized discourse; understanding this process is crucial for fostering healthierdigital ecosystems.

  PublisherOA PDFDOI

Recent grants

• Reinforcement Learning Theory in the Digital Age

  NSF · $165k · 2018–2020

Frequent coauthors

• Molly J. Crockett

  Center for Human Reproduction

  33 shared

• Jay Joseph Van Bavel

  Norwegian School of Economics

  22 shared

• Killian Lorcan McLoughlin

  Princeton University

  21 shared

• Diego A. Reinero

  Princeton University

  12 shared

• Jay J. Van Bavel

  Norwegian School of Economics

  11 shared

• Jose V. Nable

  Georgetown University

  10 shared

• Julian Wills

  New York University

  10 shared

• Peter Mende‐Siedlecki

  University of Delaware

  8 shared

Awards & honors

• Janet Taylor Spence Award for Transformative Early Career Co…
• Kabiller Science of Empathy Prize (2026)
• Andrew Carnegie Fellows Program, Finalist, Andrew Carnegie F…
• Association for Psychological Science Rising Star Award, Ass…
• SAGE Emerging Scholar award