A Sequential Data Analysis Approach to Detect Emergent Leaders in Small Groups

Abstract

This paper addresses the problem of predicting emergent leaders (ELs) in small groups, that is, meetings. This is a long-lasting research problem for social and organizational psychology and a relevant problem that recently gained momentum in social computing. Toward this goal, we propose a novel method, which analyzes the temporal dependencies of the audio-visual data by applying unsupervised deep learning generative models (feature learning). To the best of our knowledge, this is the first attempt that sequential data processing is performed for EL detection. Feature learning results in a single feature vector per a given time interval and all feature vectors representing a participant are aggregated using novel fusion techniques. Finally, the EL detection is performed using the state-of-the-art single and multiple kernel learning algorithms. The proposed method shows (significantly) improved results compared to the state-of-the-art methods and it can be adapted to analyze various small group interactions given that it is a general approach.