Employing social gaze and speaking activity for automatic determination of the Extraversion trait

Bruno Lepri, Ramanathan Subramanian, Kyriaki Kalimeri, Jacopo Staiano, Fabio Pianesi, Nicu Sebe

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

22 Citations (Scopus)

Abstract

In order to predict the Extraversion personality trait, we exploit medium-grained behaviors enacted in group meetings, namely, speaking time and social attention (social gaze). The latter will be further distinguished in to attention given to the group members and attention received from them. The results of our work confirm many of our hypotheses: a) speaking time and (some forms of) social gaze are effective in automatically predicting Extraversion; b) classification accuracy is affected by the size of the time slices used for analysis, and c) to a large extent, the consideration of the social context does not add much to accuracy prediction, with an important exception concerning social gaze.

Original languageEnglish
Title of host publicationInternational Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction, ICMI-MLMI 2010
EditorsWen Gao, Chin-Hui Lee, Jie Yang, Xilin Chen, Maxine Eskenazi, Zhengyou Zhang
Place of PublicationUnited States
PublisherAssociation for Computing Machinery (ACM)
Pages1–8
Number of pages8
ISBN (Print)9781450304146
DOIs
Publication statusPublished - 8 Nov 2010
Externally publishedYes
Event1st International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction, ICMI-MLMI 2010 - Beijing, China
Duration: 8 Nov 201010 Nov 2010

Publication series

NameInternational Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction, ICMI-MLMI 2010

Conference

Conference1st International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction, ICMI-MLMI 2010
Country/TerritoryChina
CityBeijing
Period8/11/1010/11/10

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