Abstract
In this article, we present a novel approach towards the detection and modeling of complex social phenomena in multiparty interactions, 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 Influence and Pursuit of Power, 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 learned from correlations with training examples to optimize our models and to show performance significantly above baseline.
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Strzalkowski, T. et al. (2013). Influence and Power in Group Interactions. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds) Social Computing, Behavioral-Cultural Modeling and Prediction. SBP 2013. Lecture Notes in Computer Science, vol 7812. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37210-0_3
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DOI: https://doi.org/10.1007/978-3-642-37210-0_3
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