Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Modeling socio-cultural phenomena in discourse
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Automatic extraction of cue phrases for cross-corpus dialogue act classification
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Language use: what can it tell us?
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
A Motif Approach for Identifying Pursuits of Power in Social Discourse
ICSC '12 Proceedings of the 2012 IEEE Sixth International Conference on Semantic Computing
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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.