The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
You Are Who You Talk To: Detecting Roles in Usenet Newsgroups
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 03
DEPCOS-RELCOMEX '08 Proceedings of the 2008 Third International Conference on Dependability of Computer Systems DepCoS-RELCOMEX
Extracting social power relationships from natural language
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Who's Playing Well with Others: Determining Collegiality in Text
ICSC '11 Proceedings of the 2011 IEEE Fifth International Conference on Semantic Computing
Echoes of power: language effects and power differences in social interaction
Proceedings of the 21st international conference on World Wide Web
Annotation of adversarial and collegial social actions in discourse
LAW VI '12 Proceedings of the Sixth Linguistic Annotation Workshop
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 paper, we investigate whether the social roles of dialogue participants can be recognized through the social actions performed by the participant in their interactions with others in the group. Specifically we focus on determining if a participant is the leader of the group. We decompose the problem into identifying the social goals for participant discourse segments. These social goals are represented through a set of eleven psychologically-motivated social acts. We then model leadership using a sociological-inspired model called social rank which takes into account the social capital accumulated by the participant over the course of a single dialogue. We explore these models in task-oriented dialogues communicated in English, Arabic, and Chinese and show that the incorporation of social rank can improve precision of detecting the leader by 14% in English, 8% in Arabic, and 4% in Chinese.