I am an assistant professor of politics and public affairs at Princeton University. I use a combination of experimental methods, large datasets, machine learning, and innovative measurement to study how people choose, process, spread, and respond to information about politics.
I’m a founding co-editor of the Journal of Quantitative Description: Digital Media, with Kevin Munger and Eszter Hargittai. You can read our essay introducing the journal’s philosophy and goals here.
What role do ideologically extreme media play in the polarization of society? Here we report results from a randomized longitudinal field experiment embedded in a nationally representative online panel survey (N = 1,037) in which participants were incentivized to change their browser default settings and social media following patterns, boosting the likelihood of encountering news with either a left-leaning (HuffPost) or right-leaning (Fox News) slant during the 2018 US midterm election campaign. Data on ≈19 million web visits by respondents indicate that resulting changes in news consumption persisted for at least 8 weeks. Greater exposure to partisan news can cause immediate but short-lived increases in website visits and knowledge of recent events. After adjusting for multiple comparisons, however, we find little evidence of a direct impact on opinions or affect. Still, results from later survey waves suggest that both treatments produce a lasting and meaningful decrease in trust in the mainstream media up to 1 year later. Consistent with the minimal-effects tradition, direct consequences of online partisan media are limited, although our findings raise questions about the possibility of subtle, cumulative dynamics. The combination of experimentation and computational social science techniques illustrates a powerful approach for studying the long-term consequences of exposure to partisan news.