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Open AccessArticle

Graph-Based Prediction of Meeting Participation

Department of Computer Information Systems, University of the Fraser Valley, Abbotsford, BC V2S 7M8, Canada
Multimodal Technologies Interact. 2019, 3(3), 54; https://doi.org/10.3390/mti3030054
Received: 1 May 2019 / Revised: 27 June 2019 / Accepted: 10 July 2019 / Published: 12 July 2019
(This article belongs to the Special Issue Multimodal Conversational Interaction and Interfaces)
Given a meeting participant’s turn-taking dynamics during one segment of a meeting, and their contribution to the group discussion up to that point, our aim is to automatically predict their activity level at a later point of the meeting. The predictive models use verbal and nonverbal features derived from social network representations of each small group interaction. The best automatic prediction models consistently outperform two baseline models at multiple time-lags. We analyze which interaction features are most predictive of later meeting activity levels, and investigate the efficacy of the verbal vs. nonverbal feature classes for this prediction task. At long time-lags, linguistic features become more crucial, but performance degrades compared with prediction at short time-lags. View Full-Text
Keywords: group interaction; social network analysis; social signal processing; natural language processing; conversational data; multimodal interaction group interaction; social network analysis; social signal processing; natural language processing; conversational data; multimodal interaction
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Murray, G. Graph-Based Prediction of Meeting Participation. Multimodal Technologies Interact. 2019, 3, 54.

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