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The literature provides evidence for the importance of non-verbal cues when it comes to persuading other people and developing persuasive robots. Mostly, people use these non-verbal cues subconsciously and, more importantly, are not aware of the subliminal impact of them. To raise awareness of subliminal persuasion and to explore a way for investigating persuasive cues for the development of persuasive robots and agents, we have analyzed videos of political public speeches and trained a neural network capable of predicting the degree of perceived convincingness based on visual input only. We then created visualizations of the predictions by making use of the explainable artificial intelligence methods Grad-CAM and layer-wise relevance propagation that highlight the most relevant image sections and markers. Our results show that the neural network learned to focus on the person, more specifically their posture and contours, as well as on their hands and face. These results are in line with existing literature and, thus, show the practical potential of our approach.

Towards Demystifying Subliminal Persuasiveness: Using XAI-Techniques to Highlight Persuasive Markers of Public Speeches