Pervasive sensing to model political opinions in face-to-face networks
Author(s)Madan, Anmol Prem Prakash; Farrahi, Katayoun; Gatica-Perez, Daniel; Pentland, Alex Paul
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Exposure and adoption of opinions in social networks are important questions in education, business, and government. We de- scribe 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 nd that self-reported political discussants have characteristic interaction patterns and can be predicted from sensor data. Mobile features can be used to estimate unique individ- ual exposure to di erent opinions, and help discover surprising patterns of dynamic homophily related to external political events, such as elec- tion debates and election day. To our knowledge, this is the rst time such dynamic homophily e ects have been measured. Automatically esti- mated exposure explains individual opinions on election day. Finally, we report statistically signi cant di erences in the daily activities of individ- uals that change political opinions versus those that do not, by modeling and discovering dominant activities using topic models. We nd people who decrease their interest in politics are routinely exposed (face-to-face) to friends with little or no interest in politics.
DepartmentMassachusetts Institute of Technology. Engineering Systems Division; Massachusetts Institute of Technology. Media Laboratory; Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Pervasive computing. (9th International Conference, 2011) (Lecture notes in computer science ; v. 6696)
Madan, Anmol et al. “Pervasive Sensing to Model Political Opinions in Face-to-Face Networks.” Pervasive Computing. Ed. Kent Lyons, Jeffrey Hightower, & Elaine M. Huang. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. 214-231. Copyright © 2011, Springer Berlin / Heidelberg
Author's final manuscript