Estimating the effect size of moral contagion in online networks: A pre-registered replication and meta-analysis
William J. Brady, Steve Rathje, Laura Globig, Jay Joseph Van Bavel
Abstract
<p>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.</p>
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kayo
12/9/2025
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- for each additional moral-emotional word in a social media post, the expected shares was 15% greater