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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

Political Science

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

F. Ettensperger, T. Waldvogel, et al.

This research conducted by Felix Ettensperger, Thomas Waldvogel, Uwe Wagschal, and Samuel Weishaupt dives into what sways viewer perceptions of debate winners during the 2021 German chancellor discussion. Through advanced machine learning techniques, the study uncovers how pre-debate preferences, candidate images, and pivotal speech moments influence opinion shifts. Discover the surprising elements that shape political debate performance!

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Playback language: English
Abstract
This research investigates the factors influencing viewer perceptions of debate winners during the 2021 German chancellor discussion. Utilizing survey and real-time response data from 4613 participants, the study employs machine learning (random forest and decision tree models) to identify determinants of opinion changes. The analysis reveals the impact of pre-debate preferences, candidate images, and specific speech moments on post-debate winner perception shifts. Pre-debate chancellor preference and candidate images significantly influence perception change, while party identification plays a less crucial role. The study also pinpoints specific speech moments that shifted viewer perceptions, providing a novel approach to evaluating political debate performance.
Publisher
HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS
Published On
Sep 01, 2023
Authors
Felix Ettensperger, Thomas Waldvogel, Uwe Wagschal, Samuel Weishaupt
Tags
debate
viewer perception
candidate image
opinion change
political science
machine learning

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