Search
Browse By Day
Browse By Time
Browse By Person
Browse By Mini-Conference
Browse By Division
Browse By Session or Event Type
Browse Sessions by Fields of Interest
Browse Papers by Fields of Interest
Search Tips
Conference
Location
About APSA
Personal Schedule
Change Preferences / Time Zone
Sign In
X (Twitter)
Decades of social science research suggests that our expression can be influenced by those around us. Focusing on social media, we ask: How do people learn to signal their political allegiances through language? Building on a previous large-scale observational study that found suggestive evidence of social influence over political speech, we develop a randomized intervention in ideological network structure, combining surveys and online behavioral data from Twitter to track whether people model their own political speech on that of high-status peer exemplars. Specifically, we study the relationship between the ideological composition of people's follow networks and how they express themselves on Twitter. We design a real-time digital field experiment randomly encouraging participants to follow a handful of actual Twitter accounts that frequently tweet with a liberal perspective on U.S. politics. To collect our outcomes, we link participants' public activity with their survey responses and apply automated methods to summarize the ideological signals in their tweets. We allow treatment and collection of outcomes to continue for up to two months.
William Schulz, Princeton University
Andy Guess, Princeton University
Pablo Barbera, University of Southern California
Simon Munzert, Humboldt University of Berlin
JungHwan Yang, University of Wisconsin-Madison
Sono Shah, Pew Research Center
Emma Remy, Pew Research Center
Aaron Smith, Pew Research Center