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Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2018,9,18]]},"abstract":"<jats:p>Personality traits describe individual differences in patterns of thinking, feeling, and behaving (\"between-person\" variability). But individuals also show changes in their own patterns over time (\"within-person\" variability). Existing approaches to measuring within-person variability typically rely on self-report methods that do not account for fine-grained behavior change patterns (e.g., hour-by-hour). In this paper, we use passive sensing data from mobile phones to examine the extent to which within-person variability in behavioral patterns can predict self-reported personality traits. Data were collected from 646 college students who participated in a self-tracking assignment for 14 days. To measure variability in behavior, we focused on 5 sensed behaviors (ambient audio amplitude, exposure to human voice, physical activity, phone usage, and location data) and computed 4 within-person variability features (simple standard deviation, circadian rhythm, regularity index, and flexible regularity index). We identified a number of significant correlations between the within-person variability features and the self-reported personality traits. Finally, we designed a model to predict the personality traits from the within-person variability features. Our results show that we can predict personality traits with good accuracy. The resulting predictions correlate with self-reported personality traits in the range of r = 0.32, MAE = 0.45 (for Openness in iOS users) to r = 0.69, MAE = 0.55 (for Extraversion in Android users). Our results suggest that within-person variability features from smartphone data has potential for passive personality assessment.<\/jats:p>","DOI":"10.1145\/3264951","type":"journal-article","created":{"date-parts":[[2018,9,19]],"date-time":"2018-09-19T11:58:41Z","timestamp":1537358321000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":78,"title":["Sensing Behavioral Change over Time"],"prefix":"10.1145","volume":"2","author":[{"given":"Weichen","family":"Wang","sequence":"first","affiliation":[{"name":"Dartmouth College, Computer Science, Hanover, NH, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriella M.","family":"Harari","sequence":"additional","affiliation":[{"name":"Stanford University, Department of Communication, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"Dartmouth College, Computer Science, Hanover, NH, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandrine R.","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"University of Cambridge, Department of Psychology, Cambridge, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shayan","family":"Mirjafari","sequence":"additional","affiliation":[{"name":"Dartmouth College, Computer Science, Hanover, NH, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kizito","family":"Masaba","sequence":"additional","affiliation":[{"name":"Dartmouth College, Computer Science, Hanover, NH, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew T.","family":"Campbell","sequence":"additional","affiliation":[{"name":"Dartmouth College, Computer Science, Hanover, NH, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,9,18]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocv200"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2632048.2632100"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.paid.2014.04.015"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2968219.2968275"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2016.2565381"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1037\/prj0000243"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2468356.2468502"},{"key":"e_1_2_1_8_1","series-title":"Series B (Methodological)","volume-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing. 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