Detection and application of influence rankings in small group meetings
Proceedings of the 8th international conference on Multimodal interfaces
Meeting mediator: enhancing group collaborationusing sociometric feedback
Proceedings of the 2008 ACM conference on Computer supported cooperative work
Using linguistic cues for the automatic recognition of personality in conversation and text
Journal of Artificial Intelligence Research
Modeling dominance in group conversations using nonverbal activity cues
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on multimodal processing in speech-based interactions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
An Interactive Table for Supporting Participation Balance in Face-to-Face Collaborative Learning
IEEE Transactions on Learning Technologies
Edutainment'06 Proceedings of the First international conference on Technologies for E-Learning and Digital Entertainment
Mining large-scale smartphone data for personality studies
Personal and Ubiquitous Computing
Hi YouTube!: personality impressions and verbal content in social video
Proceedings of the 15th ACM on International conference on multimodal interaction
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Leaders stand out for what they say and how they say it. This work describes the impact of the language style of emergent leaders in small group discussions based on 7 hours of audio from English spoken discussions recorded with a ubiquitous platform. For the language style analysis, word categories are extracted from manual transcriptions of the discussions as well as from automatically detected keywords. The most relevant word categories are then used to predict the emergent leader in each group. Our findings reveal that non-privacy sensitive word categories like amount of words, conjunctions and assent are good predictors of emergent leadership. The emergent leader can be correctly inferred in a fully automatic approach with up to 82% accuracy using categories derived from keywords, and up to 86% using categories derived from full manual transcriptions.