Modeling leadership and influence in multi-party online discourse

In this article, we present a novel approach towards the detection and modeling of complex social phenomena in multi-party discourse, including leadership, influence, pursuit of power and group cohesion. We have developed a two-tier approach that relies on observable and computable linguistic features of conversational text to make predictions about sociolinguistic behaviors such as Topic Control and Disagreement, that speakers deploy in order to achieve and maintain certain positions and roles in a group. These sociolinguistic behaviors are then used to infer higher-level social phenomena such as Leadership and Influence, which is the focus of this paper. We show robust performance results by comparing our automatically computed results to participants' own perceptions and rankings. We use weights learnt from correlations with training examples known leadership and influence rankings of participants to optimize our models and to show performance significantly above baseline for two different languages - English and Mandarin Chinese.