Attribute-rich longitudinal datasets of any kind are extremely rare. In 2012 and 2013, the SensibleDTU project created such a dataset using approximately 1,000 university students. Since then, a large number of studies have been performed using this dataset to ask various questions about social dynamics. This thesis delves into this dataset in an effort to explore previously unanswered questions. First, we define and identify social encounters in order to ask questions about face-to-face interaction networks. Next, we isolate students who send and receive disproportionately high numbers of phone calls and text messages to see how these groups compare to the overall population. Finally, we attempt to identify individual class schedules based solely on Bluetooth scans collected by smart phones. Our results from analyzing the phone call and text message logs as well as social encounters indicate that our methods are effective in studying and understanding social behavior.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-3537 |
| Date | 01 June 2019 |
| Creators | Cantrell, Michael A |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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