{"id":"https://openalex.org/W2583785906","doi":"https://doi.org/10.1109/bigdata.2016.7840660","title":"Towards unsupervised home location inference from online social media","display_name":"Towards unsupervised home location inference from online social media","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2583785906","doi":"https://doi.org/10.1109/bigdata.2016.7840660","mag":"2583785906"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2016.7840660","doi":"https://doi.org/10.1109/bigdata.2016.7840660","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2016.7840660","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"IEEE International Conference on Big Data (BigData)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102025799","display_name":"Chao Huang","orcid":"https://orcid.org/0000-0003-0070-2160","observed_orcids":["https://orcid.org/0000-0003-0070-2160","https://orcid.org/0000-0003-3800-5766"]},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chao Huang","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100391414","display_name":"Dong Wang","orcid":"https://orcid.org/0000-0001-6809-4988","observed_orcids":["https://orcid.org/0000-0001-6809-4988"]},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dong Wang","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101629418","display_name":"Shenglong Zhu","orcid":"https://orcid.org/0000-0003-1989-1248","observed_orcids":["https://orcid.org/0000-0003-1989-1248"]},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shenglong Zhu","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101788195","display_name":"Daniel Zhang","orcid":"https://orcid.org/0000-0002-0667-5397","observed_orcids":["https://orcid.org/0000-0002-0667-5397"]},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Yue Zhang","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I107639228"],"apc_list":null,"apc_paid":null,"fwci":3.4268,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.93447955,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"676","last_page":"685"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.0024999999441206455,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.0012000000569969416,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/social-media","display_name":"social media","score":0.8460000157356262},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"unsupervised learning","score":0.9700000286102295},{"id":"https://openalex.org/keywords/location-based-services","display_name":"location-based services","score":0.5659999847412109},{"id":"https://openalex.org/keywords/foursquare","display_name":"Foursquare","score":0.7630000114440918},{"id":"https://openalex.org/keywords/user-location-data","display_name":"user location data","score":0.44999998807907104},{"id":"https://openalex.org/keywords/personalized-recommendation","display_name":"personalized recommendation","score":0.5009999871253967}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7622794508934021},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.711201548576355},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.6866963505744934},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.5634532570838928},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5583403706550598},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5261994004249573},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4993464946746826},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47900551557540894},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4455757141113281},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.42515021562576294},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.38506412506103516},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.15786752104759216},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2016.7840660","doi":"https://doi.org/10.1109/bigdata.2016.7840660","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2016.7840660","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"IEEE International Conference on Big Data (BigData)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.5142999887466431}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W155754185","https://openalex.org/W1964002070","https://openalex.org/W1982300822","https://openalex.org/W1982471600","https://openalex.org/W1993157943","https://openalex.org/W2001344462","https://openalex.org/W2006239241","https://openalex.org/W2009043302","https://openalex.org/W2012523161","https://openalex.org/W2018277822","https://openalex.org/W2031807602","https://openalex.org/W2042631091","https://openalex.org/W2056773559","https://openalex.org/W2061352026","https://openalex.org/W2084677224","https://openalex.org/W2090790997","https://openalex.org/W2094903560","https://openalex.org/W2107961038","https://openalex.org/W2127474081","https://openalex.org/W2131369712","https://openalex.org/W2136486572","https://openalex.org/W2140535046","https://openalex.org/W2142534468","https://openalex.org/W2159730225","https://openalex.org/W2164107853","https://openalex.org/W2167521368","https://openalex.org/W2168346693","https://openalex.org/W2181061855","https://openalex.org/W2244619819","https://openalex.org/W2321707316","https://openalex.org/W2375516801","https://openalex.org/W2378654838","https://openalex.org/W2409625326","https://openalex.org/W2487398698","https://openalex.org/W2507348275","https://openalex.org/W2510927418","https://openalex.org/W2511477122","https://openalex.org/W2552855176","https://openalex.org/W2565273294","https://openalex.org/W2584297289","https://openalex.org/W4232169810","https://openalex.org/W4246769683","https://openalex.org/W6730766272"],"related_works":["https://openalex.org/W4392932162","https://openalex.org/W2409625326","https://openalex.org/W1994236950","https://openalex.org/W2038022961","https://openalex.org/W3000601870","https://openalex.org/W2379217744","https://openalex.org/W1984272552","https://openalex.org/W4407632111","https://openalex.org/W2808494595","https://openalex.org/W2763067454"],"abstract_inverted_index":{"Users'":[0],"home":[1,37,65,111,146,183],"location":[2,66,147,184],"is":[3,85],"important":[4],"information":[5,9],"for":[6],"many":[7],"advanced":[8],"services":[10],"in":[11,67,91],"big":[12],"data":[13,46,138,168],"applications":[14],"(e.g.,":[15],"localized":[16],"recommendation,":[17],"target":[18],"ads":[19],"of":[20,33,39,73,113,118,130,154,185],"local":[21],"business":[22],"and":[23,44,87,133,139,157,187],"urban":[24],"planning).":[25],"In":[26,93,121],"this":[27,94],"paper,":[28],"we":[29,96],"study":[30],"the":[31,36,42,71,110,123,127,145,152,161,182,190],"problem":[32],"accurately":[34,108,180],"inferring":[35],"locations":[38,112],"people":[40,114,186],"from":[41,171],"noisy":[43,134],"sparse":[45,132],"they":[47],"voluntarily":[48],"share":[49],"on":[50,78],"online":[51,135],"social":[52,136],"media.":[53],"Previous":[54],"studies":[55],"have":[56],"developed":[57],"supervised":[58],"learning":[59],"approaches":[60],"to":[61,89,107,144,160],"predict":[62],"a":[63,68,79,98,116],"person's":[64],"city.":[69],"However,":[70],"accuracy":[72],"these":[74],"techniques":[75],"largely":[76],"depends":[77],"high":[80],"quality":[81],"training":[82],"dataset,":[83],"which":[84],"difficult":[86],"expensive":[88],"obtain":[90],"practice.":[92],"study,":[95],"propose":[97],"new":[99],"analytical":[100],"framework,":[101],"Unsupervised":[102],"Home":[103],"Location":[104],"Inference":[105],"(UHLI),":[106],"infer":[109,181],"using":[115,131,164],"set":[117],"principle":[119],"approaches.":[120],"particular,":[122],"UHLI":[124],"scheme":[125,156,178],"addresses":[126],"critical":[128],"challenges":[129],"media":[137],"derives":[140],"an":[141],"optimal":[142],"solution":[143],"inference":[148],"problem.":[149],"We":[150],"evaluated":[151],"performance":[153],"our":[155,177],"compared":[158],"it":[159],"state-of-the-art":[162,191],"baselines":[163],"three":[165],"real":[166],"world":[167],"traces":[169],"collected":[170],"Foursquare.":[172],"The":[173],"results":[174],"showed":[175],"that":[176],"can":[179],"significantly":[188],"outperform":[189],"baselines.":[192]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2017,"cited_by_count":4}],"updated_date":"2026-10-31T12:09:11.096373","created_date":"2017-02-10T00:00:00"}
