Date of Award
Doctor of Philosophy (PhD)
I make use of new technological and scholarly developments to study political sentiments and behavior in three independent papers. In my lead paper, I address an important consequence of political deepfakes (i.e., computer-manipulated video misinformation): does the provision of information about deepfakes cause people to disbelieve real political videos? Through a set of online survey experiments, I find that information that is typical of news coverage of deepfakes induces people to disbelieve real political information. My second paper uses new social media datasets to address pressing questions about how organized American far-right groups (e.g., neo-Nazis, white supremacists, etc.) recruit new members, and whether the rise of Trump was used as a catalyst in far-right recruitment efforts. I made use of prior sociological and anthropological research that found that far-right music scenes (featuring bands with such names as Aryan Terrorism) are a key part of day-to-day functioning of the overwhelming majority of far-right hate groups in the United States. As such, I made use of public databases of song listenership on the music social network, Last.fm, before and after Trump events. I find that online friends of frequent listeners of hate music were more likely to increase their levels of hate music listenership after Trump-related events (e.g., xenophobic tweets, primary election victories, etc.). Finally, in my third paper, I leverage new theoretical frameworks in the cognitive sciences and the growth of large-scale, data-driven voter mobilization programs among non-profit organizations to address the puzzle of “voting habits.” Namely, prior research provides strong empirical evidence that voting in one election makes the average individual more likely to vote in a subsequent election, but this kind of turnout persistence does not comport with habit as it is defined in psychological sciences (elections happen too infrequently and voting is never an automatic behavior). So, in my third paper, I apply Duckworth and Gross’s (2020) Process Model of Behavior Change to turnout persistence to bridge the gap between classic economic models of voter turnout and the large body of rigorous empirical evidence showing turnout persistence. I evaluate the concrete predictions made by this model in a novel dataset of ~1.8 million voters across 9 different independent experiments.
Ternovski, John, "Leveraging New Technologies and Interdisciplinarity to Study Political Behavior, Attitudes, and Beliefs" (2021). Yale Graduate School of Arts and Sciences Dissertations. 428.