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Partisan blocking: Biased responses to shared misinformation contribute to network polarization on social media

journal contribution
posted on 11.01.2022, 11:41 by Johannes Kaiser, Cristian VaccariCristian Vaccari, Andrew ChadwickAndrew Chadwick
Researchers know little about how people respond to misinformation shared by their social media “friends.” Do responses scale up to distort the structure of online networks? We focus on an important yet under-researched response to misinformation—blocking or unfollowing a friend who shares it—and assess whether this is influenced by political similarity between friends. Using a representative sample of social media users (n = 968), we conducted two 2x2 between-subjects experiments focusing on two political issues and individuals’ political ideology as a quasi-factor. The first factor manipulated who shared the misinformation (politically similar vs. dissimilar friend); the second manipulated the misinformation’s plausibility (implausible vs. moderately plausible). Our findings, which replicated across political issues and levels of plausibility, reveal that social media users, particularly left-wing users, are more likely to block and unfollow politically dissimilar than similar friends who share misinformation. Partisan blocking contributes to network polarization on social media.


Swiss National Science Foundation

Early Postdoc Mobility Fellowship scheme (#P2ZHP1_191288)



  • Social Sciences and Humanities


  • Communication and Media

Published in

Journal of Communication


Oxford University Press (OUP)


AM (Accepted Manuscript)

Publisher statement

This is a pre-copyedited, author-produced version of an article accepted for publication in Journal of Communication following peer review. The version of record Kaiser, J. ... et al., (2022). Partisan Blocking: Biased Responses to Shared Misinformation Contribute to Network Polarization on Social Media. Journal of Communication, In Press is available online at: xxxxxxx [insert URL and DOI of the article on the OUP website]

Acceptance date









Prof Cristian Vaccari. Deposit date: 7 January 2022