{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T19:43:58Z","timestamp":1777059838881,"version":"3.51.4"},"reference-count":21,"publisher":"Association for Computing Machinery (ACM)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2012,7]]},"abstract":"<jats:p>\n            Users' locations are important for many applications such as personalized search and localized content delivery. In this paper, we study the problem of profiling Twitter users' locations with their following network and tweets. We propose a multiple location profiling model (\n            <jats:italic>MLP<\/jats:italic>\n            ), which has three key features: 1) it formally models how likely a user follows another user given their locations and how likely a user tweets a venue given his location, 2) it fundamentally captures that a user has multiple locations and his following relationships and tweeted venues can be related to any of his locations, and some of them are even noisy, and 3) it novelly utilizes the home locations of some users as partial supervision. As a result,\n            <jats:italic>MLP<\/jats:italic>\n            not only discovers users' locations\n            <jats:italic>accurately<\/jats:italic>\n            and\n            <jats:italic>completely<\/jats:italic>\n            , but also \"explains\" each following relationship by revealing users' true locations in the relationship. Experiments on a large-scale data set demonstrate those advantages. Particularly, 1) for predicting users' home locations,\n            <jats:italic>MLP<\/jats:italic>\n            successfully places 62% users and out-performs two state-of-the-art methods by 10% in accuracy, 2) for discovering users' multiple locations,\n            <jats:italic>MLP<\/jats:italic>\n            improves the baseline methods by 14% in recall, and 3) for explaining following relationships,\n            <jats:italic>MLP<\/jats:italic>\n            achieves 57% accuracy.\n          <\/jats:p>","DOI":"10.14778\/2350229.2350273","type":"journal-article","created":{"date-parts":[[2014,6,24]],"date-time":"2014-06-24T12:17:57Z","timestamp":1403612277000},"page":"1603-1614","source":"Crossref","is-referenced-by-count":66,"title":["Multiple location profiling for users and relationships from social network and content"],"prefix":"10.14778","volume":"5","author":[{"given":"Rui","family":"Li","sequence":"first","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, IL"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengjie","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, IL"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin Chen-Chuan","family":"Chang","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, IL and Advanced Digital Sciences Center, Illinois at Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2012,7]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Mixed membership stochastic blockmodels. 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Machine Learning, 50(1--2):5--43, 2003."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1367497.1367546"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772698"},{"key":"e_1_2_1_6_1","first-page":"121","volume-title":"NIPS","author":"Blei D. M.","year":"2007","unstructured":"D. M. Blei and J. D. McAuliffe . Supervised topic models . In NIPS , pages 121 -- 128 , 2007 . D. M. Blei and J. D. McAuliffe. Supervised topic models. In NIPS, pages 121--128, 2007."},{"key":"e_1_2_1_7_1","first-page":"601","volume-title":"NIPS","author":"Blei D. M.","year":"2001","unstructured":"D. M. Blei , A. Y. Ng , and M. I. Jordan . Latent dirichlet allocation . In NIPS , pages 601 -- 608 , 2001 . D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. In NIPS, pages 601--608, 2001."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1871437.1871535"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1526709.1526812"},{"key":"e_1_2_1_10_1","first-page":"545","volume-title":"VLDB","author":"Ding J.","year":"2000","unstructured":"J. Ding , L. Gravano , and N. Shivakumar . Computing geographical scopes of web resources . In VLDB , pages 545 -- 556 , 2000 . J. Ding, L. Gravano, and N. Shivakumar. Computing geographical scopes of web resources. 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