This research was funded by Yale's Center for the Study of American Politics and Institution for Social and Policy Studies. Data and supporting materials necessary to reproduce the numerical results will be made available at http://huber.research.yale.edu/ upon publication.
Disagreement and the Avoidance of Political Discussion: Aggregate Relationships and Differences across Personality Traits
Version of Record online: 23 JAN 2012
©2012, Midwest Political Science Association
American Journal of Political Science
Volume 56, Issue 4, pages 849–874, October 2012
How to Cite
Gerber, A. S., Huber, G. A., Doherty, D. and Dowling, C. M. (2012), Disagreement and the Avoidance of Political Discussion: Aggregate Relationships and Differences across Personality Traits. American Journal of Political Science, 56: 849–874. doi: 10.1111/j.1540-5907.2011.00571.x
- Issue online: 4 OCT 2012
- Version of Record online: 23 JAN 2012
Social networks play a prominent role in the explanation of many political phenomena. Using data from a nationally representative survey of registered voters conducted around the 2008 U.S. presidential election, we document three findings. First, we show that during this period, people discussed politics as frequently as (or more frequently than) other topics such as family, work, sports, and entertainment with frequent discussion partners. Second, the frequency with which a topic is discussed is strongly and positively associated with reported agreement on that topic among these same discussion partners. Supplementary experimental evidence suggests this correlation arises because people avoid discussing politics when they anticipate disagreement. Third, we show that Big Five personality traits affect how frequently people discuss a variety of topics, including politics. Some of these traits also alter the relationship between agreement and frequency of discussion in theoretically expected ways. This suggests that certain personality types are more likely to be exposed to divergent political information, and that not everyone is equally likely to experience cross-cutting discourse, even in heterogeneous networks.