Social correction across party lines in a Twitter field experiment. https://www.cmu.edu/ideas-social-cybersecurity/events/conference-papers-2021/conference-2021-paper-4-socialcorrection.pdf 

University of Michigan - Ann Arbor

Researchers looked at the extent to which political affiliation and partisanship impact the effectiveness of misinformation corrections on Twitter. They specifically focused on “social correction,” which is when social media users correct each other directly on the platform, because such corrections can also be observed by other users not directly involved in the interaction. 

Social correction across party lines in a Twitter field experiment

- Procedure
- Experimental Design
- Analysis

Social correction across party lines in a Twitter field experiment - Methodology

- Having social connections before corrections were sent increased a user’s probability of interacting with the correction message
- Political views of users and bots did not have a significant impact on user engagement with correction messages 
- Minimal social exchange (i.e. bots following users, liking their tweets) can actually boost user interaction with correction messages 
- Political ideology did not have significant impacts on user engagement with corrections 

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