Detecting leadership and cohesion in spoken interactions

We present a system for detecting leadership and group cohesion in multiparty dialogs and broadcast conversations in English and Mandarin. We systematically investigate the impact of features and designs of the prediction systems, the relationships between features and their individual significance in logistic regressions, and the contributions of feature groupings as predictors for leader and group cohesion, across genres and languages. We achieve 73.0% to 94.7% F1 accuracy for leader detection and around 80% F1 accuracy for group cohesion detection, on all data sets.