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http://hdl.handle.net/10609/92220
Title: Social network extraction and analysis based on multimodal dyadic interaction
Author: Escalera Guerrero, Sergio
Baró Solé, Xavier  
Vitrià Marca, Jordi
Radeva Ivanova, Petia
Raducanu, Bogdan
Others: Universitat Oberta de Catalunya. Estudis d'Informàtica, Multimèdia i Telecomunicació
Universitat Autònoma de Barcelona
Keywords: influence model
social interaction
audio/visual data fusion
social network analysis
Issue Date: 7-Feb-2012
Publisher: Sensors
Citation: Escalera Guerrero, S., Baró Solé, X., Vitrià Marca, J., Radeva Ivanova, P. & Raducanu, B. (2012). Social Network Extraction and Analysis Based on Multimodal Dyadic Interaction. Sensors, 12(2), 1702-1719. doi: 10.3390/s120201702
Project identifier: info:eu-repo/grantAgreement/TIN2009-14404-C02
info:eu-repo/grantAgreement/CONSOLIDERINGENIO CSD 2007-00018
info:eu-repo/grantAgreement/TIN2010-09703-E
Also see: https://www.mdpi.com/1424-8220/12/2/1702/pdf
Abstract: Social interactions are a very important component in people's lives. Social network analysis has become a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from multimodal dyadic interactions. For our study, we used a set of videos belonging to New York Times' Blogging Heads opinion blog. The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The links' weights are a measure of the "influence" a person has over the other. The states of the Influence Model encode automatically extracted audio/visual features from our videos using state-of-the art algorithms. Our results are reported in terms of accuracy of audio/visual data fusion for speaker segmentation and centrality measures used to characterize the extracted social network.
Language: English
URI: http://hdl.handle.net/10609/92220
ISSN: 1424-8220MIAR
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