Pervasive sensing to model political opinions in face-to-face networks

Pervasive sensing to model political opinions in face-to-face networks (2011)

Cached

Download Links

[hd.media.mit.edu]

by Anmol Madan , Katayoun Farrahi , Daniel Gatica-perez , Sandy Pentland

Venue:	In Pervasive
Citations:	5 - 2 self

Versions

Version History

Metadata Version 1

Datum	Value	Source
TITLE	Pervasive sensing to model political opinions in face-to-face networks	INFERENCE
AUTHOR NAME	Anmol Madan	SVM HeaderParse 0.2
AUTHOR AFFIL	1; MIT Media Laboratory, Massachusetts Institute of Technology, Cambridge	SVM HeaderParse 0.2
AUTHOR ADDR	USA	SVM HeaderParse 0.2
AUTHOR NAME	Katayoun Farrahi	SVM HeaderParse 0.2
AUTHOR NAME	Daniel Gatica-perez	SVM HeaderParse 0.2
AUTHOR NAME	Sandy Pentland	SVM HeaderParse 0.2
AUTHOR AFFIL	3	SVM HeaderParse 0.2
AUTHOR ADDR	Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland	SVM HeaderParse 0.2
ABSTRACT	Abstract. Exposure and adoption of opinions in social networks are important questions in education, business, and government. We describe a novel application of pervasive computing based on using mobile phone sensors to measure and model the face-to-face interactions and subsequent opinion changes amongst undergraduates, during the 2008 US presidential election campaign. We find that self-reported political discussants have characteristic interaction patterns and can be predicted from sensor data. Mobile features can be used to estimate unique individual exposure to different opinions, and help discover surprising patterns of dynamic homophily related to external political events, such as election debates and election day. To our knowledge, this is the first time such dynamic homophily effects have been measured. Automatically estimated exposure explains individual opinions on election day. Finally, we report statistically significant differences in the daily activities of individuals that change political opinions versus those that do not, by modeling and discovering dominant activities using topic models. We find people who decrease their interest in politics are routinely exposed (face-to-face) to friends with little or no interest in politics. 1	SVM HeaderParse 0.2
YEAR	2011	INFERENCE
VENUE	In Pervasive	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	50 found	ParsCit 1.0