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Don’t @ Me: Experimentally Reducing Partisan Incivility on Twitter

Published online by Cambridge University Press:  16 July 2020

Kevin Munger Open the ORCID record for Kevin Munger [Opens in a new window]
Kevin Munger*
Affiliation:
Pennsylvania State University, Pond Lab, State College, PA, USA
*
Corresponding author. Email: kmm7999@psu.edu
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Abstract

I conduct an experiment which examines the impact of moral suasion on partisans engaged in uncivil arguments. Partisans often respond in vitriolic ways to politicians they disagree with, and this can engender hateful responses from partisans from the other side. This phenomenon was especially common during the contentious 2016 US Presidential Election. Using Twitter accounts that I controlled, I sanctioned people engaged partisan incivility in October 2016. I found that messages containing moral suasion were more effective at reducing incivility than were messages with no moral content in the first week post-treatment. There were no significant treatment effects in the first day post-treatment, emphasizing the need for research designs that measure effect duration. The type of moral suasion employed, however, did not have the expected differential effect on either Republicans or Democrats. These effects were significantly moderated by the anonymity of the subjects.

Keywords

affective polarizationTwitterfield experiment

Information

Type
Research Article
Information
Journal of Experimental Political Science , Volume 8 , Issue 2 , Summer 2021 , pp. 102 - 116
Copyright
© The Experimental Research Section of the American Political Science Association 2020

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Footnotes

*

I would like to thank many people for their feedback on this paper, especially the members of the NYU SMaPP lab, Livio D. Lonardo, J. Hodgdon Bisbee, Drew Dimmery, Neal Beck, Josh Tucker, Jonathan Nagler, Patrick Egan, Chris Dawes, Andy Guess, Alex Siegel, Joanna Sterling, John Jost, and participants at the several conferences I presented this work in during 2017. I declare no conflicts of interest. The data, code, and any additional materials required to replicate all analyses in this article are available at the Journal of Experimental Political Science Dataverse within the Harvard Dataverse Network, at: doi:10.7910/DVN/OUYTUP.

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https://dx.doi.org/10.7910/DVN/OUYTUP
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