This paper addresses the problem of predicting emergent leaders (ELs) in small groups i.e. meetings. This is a long-lasting research problem for social and organizational psychology and a relevant problem that recently gained a momentum in social computing. Towards this goal, we propose a novel method, which analyzes the temporal dependencies of the audiovisual 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. Lastly, the emergent leader detection is performed using the state of the art single and multiple kernel learning algorithms. The proposed method shows (significantly) improved results as 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.
|Titolo:||A Sequential Data Analysis Approach to Detect Emergent Leaders in Small Groups|
|Data di pubblicazione:||2019|
|Appare nelle tipologie:||01.01 Articolo in Rivista|