Global Studies Seminar presents Cantay Caliskan

The Global Studies Seminar presents “Why So Serious?: Emotions of Presidential Candidates on the Road the 2020 Presidential Elections,” by Cantay Caliskan, visiting assistant professor at Denison.

This article examines the way in which the emotions of presidential candidates in primary debates influence public and elite perception by using the twelve Democratic debates for the 2020 elections as a case study. Primary debates are crucial for American politics since they offer the voters the opportunity to see and observe whether the candidates have the required expertise for the job and are psychologically ready to withstand the tensions in long conversations. In this regard, it is crucial to understand which emotions are believed to be the most promising in order to be valued as the fittest candidate for the presidency. The paper uses image-, audio-, and text-data from primary debates to quantify the emotions of candidates. To measure the direct effects of the candidate performance on political success, we look at the changes in approval ratings, campaign contributions, and candidate endorsements before and after the debates. The paper uses machine learning, and deep learning to quantify emotions and Bayesian regression discontinuity techniques for causal inference.

Caliskan received his PhD from Boston University in 2018. His dissertation focused on the flow of money and interests in American and Scandinavian policymaking by bringing aspects of policy choices, political connections, and societal perceptions together. Previously, he earned his bachelor of art degree at Brandeis University and master of art degree at Koç University. His research interests include computational social science, renewable energy, social media, US Congress and networks of lobbying. Caliskan is teaching Introduction to Data Analytics and Advanced Methods for Data Analytics at Denison University. He has presented his research in various conferences and events including APSA, MPSA, Political Networks, Boston University Data Science Day and Microsoft Research New England Machine Learning Day.