Pervasive sensing to model political opinions in face-to-face networks
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Pentland_Pervasive Sensing.pdf
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Author(s)
Madan, Anmol Prem Prakash
Farrahi, Katayoun
Gatica-Perez, Daniel
Pentland, Alex Paul
Date Issued
June 2011
Journal
Pervasive computing. (9th International Conference, 2011) (Lecture notes in computer science ; v. 6696)
Publisher
Springer
Citation
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
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Engineering Systems Division
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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DOI of Published Version
https://doi.org/10.1007/978-3-642-21726-5_14
