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Sensors 2012, 12(2), 1702-1719; doi:10.3390/s120201702

Social Network Extraction and Analysis Based on Multimodal Dyadic Interaction

Centre de Visió per Computador, Campus UAB, Edifici O, Bellaterra, 08193 Barcelona, Spain
Department Matemàtica Aplicada i Anàlisi, Universitat de Barcelona, Gran Via 585, 08007 Barcelona, Spain
Estudis d’Informàtica, Multimèdia i Telecomunicació, Universitat Oberta de Catalunya, Rambla del Poblenou 156, 08018 Barcelona, Spain
Author to whom correspondence should be addressed.
Received: 24 December 2011 / Revised: 21 January 2012 / Accepted: 22 January 2012 / Published: 7 February 2012
(This article belongs to the Section Physical Sensors)
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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. View Full-Text
Keywords: social interaction; audio/visual data fusion; influence model; social network analysis social interaction; audio/visual data fusion; influence model; social network analysis

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Escalera, S.; Baró, X.; Vitrià, J.; Radeva, P.; Raducanu, B. Social Network Extraction and Analysis Based on Multimodal Dyadic Interaction. Sensors 2012, 12, 1702-1719.

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