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Colocation is determined from Bluetooth and GPS to extract social rhythms, patterns in time, duration, place, and people corresponding to real-world activities. Social ties are formulated from proximity and shared spheres and rhythms. Quantitative evaluation is performed for 10+ million samples over 45 man-months. Applications are presented with assessment of perceived utility:\n            <jats:italic>Socio-Graph<\/jats:italic>\n            , a video and photo browser with filters for social metadata, and\n            <jats:italic>Jive<\/jats:italic>\n            , a blog browser that uses rhythms to discover similarity between entries automatically.\n          <\/jats:p>","DOI":"10.1145\/1413862.1413864","type":"journal-article","created":{"date-parts":[[2008,12,3]],"date-time":"2008-12-03T21:56:04Z","timestamp":1228341364000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["Sensing and using social context"],"prefix":"10.1145","volume":"5","author":[{"given":"Brett","family":"Adams","sequence":"first","affiliation":[{"name":"Curtin University of Technology, Perth, W. Australia"}]},{"given":"Dinh","family":"Phung","sequence":"additional","affiliation":[{"name":"Curtin University of Technology, Perth, W. 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