How to convince in a televised debate: the application of machine learning to analyze why viewers changed their winner perception during the 2021 German chancellor discussion - Felix Ettensperger; Thomas Waldvogel; Uwe Wagschal; Samuel Weishaupt (Humanities & Social Sciences Communications, 2023)
Abstract What causes viewers to change their winner perception during a televised debate? The article addresses this question, drawing on a large-N field study of the 2021 chancellor debate in Germany, which contains survey and real-time response data for 4613 participants. Using machine learning techniques, we identify determinants of why participants change their opinion about who is winning the discussion during the debate. Our analysis based on random forest and decision tree models shows in detail, first, what factors drive debate winner perceptions in the course of televised debate reception. Second, we reveal what combinations of political predispositions and candidate statements are necessary to change the viewers’ debate winner perception. In doing so, third, we expand the toolbox of empirical debate research with our analysis based on machine learning algorithms. Our findings indicate that pre-debate chancellor preference and candidate images play a crucial role in determining post-debate perception change, while party identification is less important in predicting changes. We can also directly identify several speech moments in the debate that shifted viewer perception, a novel approach to evaluating political debate performance.