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

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journal contribution
posted on 01.04.2022, 14:27 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.

Funding

Swiss National Science Foundation

Early Postdoc Mobility Fellowship scheme (#P2ZHP1_191288)

History

School

  • Social Sciences and Humanities

Department

  • Communication and Media

Published in

Journal of Communication

Volume

72

Issue

2

Pages

214-240

Publisher

Oxford University Press (OUP)

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Oxford University Press under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

07/01/2022

Publication date

2022-03-15

Copyright date

2022

ISSN

0021-9916

eISSN

1460-2466

Language

en

Depositor

Prof Cristian Vaccari. Deposit date: 7 January 2022