University of Michigan - Ann Arbor

- Blocked users based on follower count (status), tweets in the last two weeks (activity), mentions in the last two weeks, replies in the last two weeks, and political stance 
- Users in each block randomly assigned to one of four conditions (social, asocial, partisan agreement, partisan discordant)
- Used a 2x2 between-subject design 
- Social condition: bots followed users and liked their three most recent tweets one day before corrections were sent 
- Partisan agreement condition: bots with the same political view sent correction message to users
- Partisan discordant condition: bots with opposite political view sent correction message to users 

Social correction across party lines in a Twitter field experiment - Experimental Design 

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 

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