Prediction of the leadership style of an emergent leader using audio and visual nonverbal features

The coordination of a leader with group members is very important for an effective leadership given that this figure is the person who actually manages the team members to achieve a desired goal. Investigating the leadership and especially the leadership style is a prominent research topic in social and organizational psychology. However, this is a new problem in social signal processing that can actually make valuable contributions by analyzing multimodal data in a more effective and efficient way. In this work, we identify the leadership style of an emergent leader (i.e., the leader who naturally arises from a group, not designated) as autocratic or democratic. The proposed method is applied to a dataset in-the-wild; in other words, there is no role-playing, which is novel for this problem. Multiple kernel learning (MKL) using multimodal nonverbal features is utilized to predict leadership styles that proved to achieve better predictions as compared to traditional learning methods. Thanks to MKL and a simple heuristic proposed, the best performing features are also identified, showing that better predictions can be reached only by using those features. Additionally, correlation analysis between the extracted nonverbal features and the results of social psychology questionnaire is also performed. This shows that significantly high correlations exist for speaking activity based and prosodic nonverbal features.