Pervasive Sensing to Model Political Opinions in Face-to-Face Networks

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Madan, A; Farrahi, Katayoun; Gatica-Perez, D and Pentland, A. 2011. Pervasive Sensing to Model Political Opinions in Face-to-Face Networks. In: K Lyons; J Hightower and E. M. Huang, eds. Pervasive Computing: Proceedings 9th International Conference, Pervasive 2011, San Francisco, USA, June 12-15, 2011. 6966 San Francisco: Springer, pp. 214-231. ISBN 978-3-642-21726-5 [Book Section]

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Abstract or Description

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.

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Book Section

Identification Number (DOI):

https://doi.org/10.1007/978-3-642-21726-5_14