{"id":"https://openalex.org/W2618068520","doi":"https://doi.org/10.1109/access.2017.2707480","title":"Exploring Scalability and Time-Sensitiveness in Reliable Social Sensing With Accuracy Assessment","display_name":"Exploring Scalability and Time-Sensitiveness in Reliable Social Sensing With Accuracy Assessment","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2618068520","doi":"https://doi.org/10.1109/access.2017.2707480","mag":"2618068520"},"language":"en","primary_location":{"id":"doi:10.1109/access.2017.2707480","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2017.2707480","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2017.2707480","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102025799","display_name":"Chao Huang","orcid":"https://orcid.org/0000-0003-0070-2160"},"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":true,"raw_author_name":"Chao Huang","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA"],"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/A5100391517","display_name":"Dong Wang","orcid":"https://orcid.org/0000-0002-9599-8023"},"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"],"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":2,"corresponding_author_ids":["https://openalex.org/A5102025799"],"corresponding_institution_ids":["https://openalex.org/I107639228"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.3762,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.70169357,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"5","issue":null,"first_page":"14405","last_page":"14418"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9894000291824341,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9775999784469604,"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/correctness","display_name":"Correctness","score":0.8591957092285156},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8384740352630615},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.8148359656333923},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6321916580200195},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5284892320632935},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5130394697189331},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5039030909538269},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.49142563343048096},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4892292022705078},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3229624629020691},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3113211393356323},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.20987266302108765},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.08846470713615417}],"concepts":[{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.8591957092285156},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8384740352630615},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.8148359656333923},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6321916580200195},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5284892320632935},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5130394697189331},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5039030909538269},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.49142563343048096},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4892292022705078},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3229624629020691},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3113211393356323},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.20987266302108765},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.08846470713615417},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2017.2707480","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2017.2707480","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:21ed83cfe3004bf9a4b1a4da7ec0ef02","is_oa":true,"landing_page_url":"https://doaj.org/article/21ed83cfe3004bf9a4b1a4da7ec0ef02","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 5, Pp 14405-14418 (2017)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2017.2707480","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2017.2707480","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G22008513","display_name":null,"funder_award_id":"CNS-1566465","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3142689400","display_name":null,"funder_award_id":"IIS-1447795","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3262362702","display_name":null,"funder_award_id":"W911NF-16-1-0388","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G7634783742","display_name":null,"funder_award_id":"CBET-1637251","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1521736627","https://openalex.org/W1667830255","https://openalex.org/W1775665607","https://openalex.org/W1848472898","https://openalex.org/W1967647772","https://openalex.org/W1982467666","https://openalex.org/W1985777504","https://openalex.org/W1995211436","https://openalex.org/W2003684739","https://openalex.org/W2009043302","https://openalex.org/W2031807602","https://openalex.org/W2049633694","https://openalex.org/W2054051012","https://openalex.org/W2068782468","https://openalex.org/W2080801582","https://openalex.org/W2085403478","https://openalex.org/W2105441605","https://openalex.org/W2118388899","https://openalex.org/W2118432740","https://openalex.org/W2124225884","https://openalex.org/W2138621811","https://openalex.org/W2138752966","https://openalex.org/W2142048776","https://openalex.org/W2151073469","https://openalex.org/W2155189155","https://openalex.org/W2159359879","https://openalex.org/W2162237605","https://openalex.org/W2181061855","https://openalex.org/W2244619819","https://openalex.org/W2255253409","https://openalex.org/W2467851685","https://openalex.org/W2736009403","https://openalex.org/W4241569833","https://openalex.org/W4245951318","https://openalex.org/W4246769683","https://openalex.org/W4249852436","https://openalex.org/W6637064631","https://openalex.org/W6637998964","https://openalex.org/W6691658213"],"related_works":["https://openalex.org/W3008339103","https://openalex.org/W1667647204","https://openalex.org/W2404647514","https://openalex.org/W4247536566","https://openalex.org/W4241418540","https://openalex.org/W2018477250","https://openalex.org/W3119814709","https://openalex.org/W1508895727","https://openalex.org/W2725786787","https://openalex.org/W1590965489"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,27,77,165,206,211,241],"scalable":[4,152,166],"estimation":[5,184],"theoretic":[6],"framework":[7,126],"to":[8,38,82,92,127,153,193,220],"address":[9,159],"the":[10,42,62,67,94,119,130,133,160,175,179,196,199,222,228,248],"time-sensitive":[11,167],"truth":[12,84,95,120,134,143,168,200],"discovery":[13,96,121,135,144,169,201],"problem":[14],"with":[15,34,216],"accuracy":[16,131,197],"assessment":[17],"in":[18,55,60,110,118],"social":[19,56,155],"sensing":[20,23,57,156],"applications.":[21],"Social":[22],"has":[24,89,113],"emerged":[25],"as":[26,83],"new":[28,190],"application":[29],"paradigm":[30],"that":[31,172],"provides":[32],"us":[33],"an":[35,183],"unprecedented":[36],"opportunity":[37],"collect":[39],"observations":[40],"about":[41],"physical":[43],"world":[44],"from":[45],"humans":[46],"or":[47],"devices":[48],"on":[49,210],"their":[50],"behalf.":[51],"A":[52],"fundamental":[53],"challenge":[54],"applications":[58],"lies":[59],"ascertaining":[61],"correctness":[63],"of":[64,69,75,106,132,198,218,252],"claims":[65,109],"and":[66,108,139,178,240,250],"reliability":[68],"data":[70,238],"sources":[71],"without":[72],"knowing":[73],"either":[74],"them":[76],"priori,":[78],"which":[79,149],"is":[80,137],"referred":[81],"discovery.":[85],"While":[86],"significant":[87],"progress":[88],"been":[90,115],"made":[91],"solve":[93],"problem,":[97],"there":[98],"exists":[99],"three":[100,232],"important":[101],"limitations:":[102],"(1)":[103],"The":[104,244],"information":[105],"users":[107],"time":[111],"dimension":[112],"not":[114,151],"fully":[116],"exploited":[117],"solutions;":[122],"(2)":[123],"An":[124],"analytical":[125],"rigorously":[128,194],"assess":[129,195],"results":[136,246],"lacking;":[138],"(3)":[140],"Many":[141],"current":[142],"schemes":[145],"perform":[146],"sequential":[147],"operations,":[148],"are":[150],"large-scale":[154],"events.":[157],"To":[158],"above":[161],"limitations,":[162],"we":[163,188,226],"propose":[164],"(TS-TD)":[170],"scheme":[171,230],"explicitly":[173],"incorporates":[174],"source":[176],"responsiveness":[177],"claim":[180],"lifespan":[181],"into":[182],"theoretical":[185],"framework.":[186],"Furthermore,":[187],"develop":[189],"confidence":[191],"bounds":[192],"results.":[202],"We":[203],"also":[204],"implement":[205],"parallel":[207],"TS-TD":[208,229],"algorithm":[209],"graphic":[212],"processing":[213],"unit":[214],"platform":[215],"thousands":[217],"cores":[219],"improve":[221],"computational":[223],"efficiency.":[224],"Finally,":[225],"evaluate":[227],"through":[231],"real-world":[233],"case":[234],"studies":[235],"using":[236],"Twitter":[237],"feeds":[239],"simulation":[242],"study.":[243],"evaluation":[245],"demonstrate":[247],"effectiveness":[249],"efficiency":[251],"our":[253],"scheme.":[254]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
