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Gender, Candidate Emotional Expression, and Voter Reactions During Televised Debates
Abstract
Voters evaluate politicians not just by what they say, but also how they say it, via facial displays of emotions and vocal pitch. Candidate characteristics can shape how leaders use – and how voters react to – nonverbal cues. Drawing on role congruity expectations, we focus on how gender shapes the use of and reactions to facial, voice, and textual communication in political debates. Using full-length debate videos from four German national elections (2005–2017) and a minor debate in 2017, we employ computer vision, machine learning, and text analysis to extract facial displays of emotion, vocal pitch, and speech sentiment. Consistent with our expectations, Angela Merkel expresses less anger and is less emotive than her male opponents. We combine second-by-second candidate emotions data with continuous responses recorded by live audiences. We find that voters punish Merkel for anger displays and reward her happiness and general emotional displays.
This study proposes a computational-video-analysis pipeline using OpenPose for keypoint detection, the RNN-LSTM network for constructing 12 gesture classifiers, and data augmentation and epoch early-stopping techniques for performance optimization. Through the measurement of accuracy, precision, recall, and F1 scores, this study compares three approaches (the vanilla approach, data-augmentation approach, and epoch-optimization approach), which gradually increase the model performance for all gesture features. The study suggests that a combination of data augmentation and epoch early-stopping techniques can effectively solve the imbalanced dataset problem faced by customized datasets and substantially increase the accuracy and F1 scores by 10–20%, achieving a satisfying accuracy of 70%–90% for most gesture detections.
Terrorist attacks routinely produce rallies for incumbent men in the executive office. With scarce cases, there has been little consideration of terrorism’s consequences for evaluations of sitting women executives. Fusing research on rallies with scholarship on women in politics, we derive a gender-revised framework wherein the public will be less inclined to rally around women when terrorists attack. A critical case is UK Prime Minister Theresa May, a right-leaning incumbent with security experience. Employing a natural experiment, we demonstrate that the public fails to rally after the 2017 Manchester Arena attack. Instead, evaluations of May decrease, with sharp declines among those holding negatives views about women. We further show May’s party loses votes in areas closer to the attack. We then find support for the argument in a multinational test. We conclude that conventional theory on rally events requires revision: women leaders cannot count on rallies following major terrorist attacks.
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