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• W2977006558 abstract "The age of the Internet of things has brought about the field of smartphone sensing, which has found numerous uses for the growing population of smartphones throughout the world. Among these uses are longitudinal studies that have studied social networks, health behaviors, productivity, and other topics with large participant bases and detailed data. In this paper we demonstrate another way to study social behavior during one of these longitudinal studies, the NetHealth study, by predicting whether a pair of students are friends or not through visit data patterns of a social hub of any college campus - the dining hall. We use Bluetooth Low Energy (BLE) beacons deployed throughout the dining hall to detect when students enter and leave, and create features from this visit data that can cope with varying amounts of data among study participants. We then demonstrate the predictive power of these features, calculated over a semester of data, against ground truth friendship labels given by the participants themselves, obtaining an AUROC of 0.716." @default.
• W2977006558 created "2019-10-03" @default.
• W2977006558 creator A5044105092 @default.
• W2977006558 creator A5048928683 @default.
• W2977006558 date "2019-07-01" @default.
• W2977006558 modified "2023-09-24" @default.
• W2977006558 title "Predicting Friendship Pairs from BLE Beacons Using Dining Hall Visits" @default.
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• W2977006558 doi "https://doi.org/10.1109/icccn.2019.8846963" @default.
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