Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/342979 
Year of Publication: 
2026
Series/Report no.: 
I4R Discussion Paper Series No. 309
Publisher: 
Institute for Replication (I4R), s.l.
Abstract: 
Brady et al. (2023) investigated whether people overperceive moral outrage in social media and whether exposure to outrage-saturated newsfeeds amplifies perceived collective outrage and intergroup hostility across five studies. Participants who viewed the high-overperception feed judged the collective outrage of their social network as substantially large. They also found that exposure to highly overperceived feeds increased perceived social appropriateness, increased affective polarization, and heightened perceived ideological extremity. In this commentary, I successfully computationally reproduce all reported effects even after correcting a data-processing error that unintentionally duplicated observations in Studies 1-3, including the large between-condition difference in perceived collective outrage (Study 4). Bootstrapping 2,000 resamples confirmed that the 95% bias-corrected and accelerated CI [2.09, 2.51] matches the analytic interval, indicating an exceptionally stable effect. I also replicate Study 5 using tweet-level mixed-effects models that preserve the nested structure of the data. These reanalyses reproduce the main effects of overperceived feeds on social appropriateness (b = 0.67, p < .001), ideological temperature (ηp2 = .27), and ideological extremity (b = 0.61, p < .001). Finally, robustness checks excluding politically neutral participants, bootstrapping model estimates, and adding political ideology as a moderator all leave the magnitude and significance of the key findings unchanged, with the moderating effect of political ideology sparking additional future research questions. Overall, 100% of directional effects reproduce with near-identical size, demonstrating that the original paper's central claims about overperception-driven amplification of perceived outrage and polarization are computationally and statistically robust.
Document Type: 
Working Paper

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