Facial finetuning: using pretrained image classification models to predict politicians’ success

Abstract There is a long-standing interest in how the visual appearance of politicians predicts their success. Usually, the scope of such studies is limited by the need for human-rated facial features. We instead fine-tune pre-trained image classification models based on convolutional neural networks to predict facial features of 7,080 Danish politicians. Attractiveness and trustworthiness scores correlate positively and robustly with both ballot paper placement (proxying for intra-party success) and the number of votes gained in local and national elections, while dominance scores correlate inconsistently. Effect sizes are at times substantial. We find no moderation by politician gender or election type. However, dominance scores correlate significantly with outcomes for conservative politicians. We discuss possible causal mechanisms behind our results.