{"id":"https://openalex.org/W2987755095","doi":"https://doi.org/10.1145/3359164","title":"Efficient Elicitation Approaches to Estimate Collective Crowd Answers","display_name":"Efficient Elicitation Approaches to Estimate Collective Crowd Answers","publication_year":2019,"publication_date":"2019-11-07","ids":{"openalex":"https://openalex.org/W2987755095","doi":"https://doi.org/10.1145/3359164","mag":"2987755095"},"language":"en","primary_location":{"id":"doi:10.1145/3359164","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3359164","pdf_url":null,"source":{"id":"https://openalex.org/S4210183893","display_name":"Proceedings of the ACM on Human-Computer Interaction","issn_l":"2573-0142","issn":["2573-0142"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Human-Computer Interaction","raw_type":"journal-article"},"type":"article","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/A5081068980","display_name":"John Joon Young Chung","orcid":"https://orcid.org/0000-0002-8492-2525"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"John Joon Young Chung","raw_affiliation_strings":["University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058977902","display_name":"Jean Song","orcid":"https://orcid.org/0000-0003-4379-3971"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jean Y. Song","raw_affiliation_strings":["University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074827090","display_name":"Sindhu Kutty","orcid":null},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sindhu Kutty","raw_affiliation_strings":["University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059548204","display_name":"Sung-Soo Hong","orcid":"https://orcid.org/0000-0001-6050-5404"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sungsoo (Ray) Hong","raw_affiliation_strings":["New York University, New York City, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"New York University, New York City, NY, USA","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079709359","display_name":"Juho Kim","orcid":"https://orcid.org/0000-0001-6348-4127"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Juho Kim","raw_affiliation_strings":["Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063574664","display_name":"Walter S. Lasecki","orcid":null},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Walter S. Lasecki","raw_affiliation_strings":["University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.7489,"has_fulltext":false,"cited_by_count":46,"citation_normalized_percentile":{"value":0.97598706,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"3","issue":"CSCW","first_page":"1","last_page":"25"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9865000247955322,"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"}},{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9836000204086304,"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/crowdsourcing","display_name":"Crowdsourcing","score":0.7943449020385742},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.7146828770637512},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6683796644210815},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.6202894449234009},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.567976176738739},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5090901255607605},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.506264328956604},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.5000910758972168},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48871684074401855},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4657977521419525},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.41114598512649536},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32185643911361694},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.12869590520858765}],"concepts":[{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.7943449020385742},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.7146828770637512},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6683796644210815},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.6202894449234009},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.567976176738739},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5090901255607605},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.506264328956604},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.5000910758972168},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48871684074401855},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4657977521419525},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.41114598512649536},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32185643911361694},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.12869590520858765},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3359164","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3359164","pdf_url":null,"source":{"id":"https://openalex.org/S4210183893","display_name":"Proceedings of the ACM on Human-Computer Interaction","issn_l":"2573-0142","issn":["2573-0142"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Human-Computer Interaction","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.5400000214576721}],"awards":[],"funders":[{"id":"https://openalex.org/F4320315934","display_name":"Toyota Research Institute","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":84,"referenced_works":["https://openalex.org/W137420995","https://openalex.org/W279631868","https://openalex.org/W1524782856","https://openalex.org/W1573900212","https://openalex.org/W1580288159","https://openalex.org/W1589303317","https://openalex.org/W1699023120","https://openalex.org/W1702329404","https://openalex.org/W1962580118","https://openalex.org/W1970381522","https://openalex.org/W1982003829","https://openalex.org/W1994322930","https://openalex.org/W1995270501","https://openalex.org/W1995945562","https://openalex.org/W1997275924","https://openalex.org/W1997504572","https://openalex.org/W2008090445","https://openalex.org/W2025951011","https://openalex.org/W2031356414","https://openalex.org/W2041411104","https://openalex.org/W2045515072","https://openalex.org/W2063782051","https://openalex.org/W2066454034","https://openalex.org/W2066640191","https://openalex.org/W2070316439","https://openalex.org/W2071638895","https://openalex.org/W2076075031","https://openalex.org/W2085340039","https://openalex.org/W2103291452","https://openalex.org/W2120396827","https://openalex.org/W2124482936","https://openalex.org/W2138035124","https://openalex.org/W2144569346","https://openalex.org/W2151401338","https://openalex.org/W2159190230","https://openalex.org/W2186574009","https://openalex.org/W2187291759","https://openalex.org/W2250638193","https://openalex.org/W2251738400","https://openalex.org/W2251939518","https://openalex.org/W2252039660","https://openalex.org/W2273812590","https://openalex.org/W2290009368","https://openalex.org/W2330485005","https://openalex.org/W2398344594","https://openalex.org/W2400269077","https://openalex.org/W2553156677","https://openalex.org/W2558888286","https://openalex.org/W2574781439","https://openalex.org/W2580299352","https://openalex.org/W2607311634","https://openalex.org/W2610525153","https://openalex.org/W2615497679","https://openalex.org/W2658131525","https://openalex.org/W2741620441","https://openalex.org/W2767546953","https://openalex.org/W2787932459","https://openalex.org/W2794122052","https://openalex.org/W2795821046","https://openalex.org/W2797062063","https://openalex.org/W2806019599","https://openalex.org/W2807661964","https://openalex.org/W2810059778","https://openalex.org/W2898349875","https://openalex.org/W2899134503","https://openalex.org/W2899260230","https://openalex.org/W2899428503","https://openalex.org/W2917794201","https://openalex.org/W2922162703","https://openalex.org/W2942161347","https://openalex.org/W2943323352","https://openalex.org/W2952835695","https://openalex.org/W2962843773","https://openalex.org/W2963726954","https://openalex.org/W2967708711","https://openalex.org/W3041215734","https://openalex.org/W3105616927","https://openalex.org/W3106510757","https://openalex.org/W3121518240","https://openalex.org/W3122310065","https://openalex.org/W3124912334","https://openalex.org/W4234545626","https://openalex.org/W4240458456","https://openalex.org/W4240549324"],"related_works":["https://openalex.org/W3032998312","https://openalex.org/W135177976","https://openalex.org/W4384486036","https://openalex.org/W1503094549","https://openalex.org/W2337920774","https://openalex.org/W4286908577","https://openalex.org/W2886410948","https://openalex.org/W2025875869","https://openalex.org/W4318823662","https://openalex.org/W3207526114"],"abstract_inverted_index":{"When":[0],"crowdsourcing":[1],"the":[2,46,63,98,164,179,193,200,212,237,243,247,274],"creation":[3],"of":[4,33,49,62,74,87,100,134,163,182,273],"machine":[5],"learning":[6],"datasets,":[7],"statistical":[8],"distributions":[9,26,289],"that":[10,66,84,166,225,305],"capture":[11],"diverse":[12],"answers":[13],"can":[14,306],"represent":[15],"ambiguous":[16],"data":[17],"better":[18,271],"than":[19],"a":[20,30,41,71,123,132,233,251,270],"single":[21,124,252],"best":[22],"answer.":[23],"Unfortunately,":[24],"collecting":[25],"is":[27,89,118,148],"expensive":[28],"because":[29],"large":[31,72],"number":[32],"responses":[34],"need":[35],"to":[36,39,54,59,96,153,208,217,220,241,269,286,299],"be":[37,68],"collected":[38,175],"form":[40],"stable":[42],"distribution.":[43],"Despite":[44],"this,":[45],"efficient":[47,214],"collection":[48],"answer":[50,159,253,277,288,309],"distributions-that":[51],"is,":[52],"ways":[53],"use":[55,287],"less":[56],"human":[57,238],"effort":[58],"collect":[60],"estimates":[61],"eventual":[64],"distribution":[65],"would":[67,171,227],"formed":[69],"by":[70,120,150],"group":[73],"responses-is":[75],"an":[76,161],"under-studied":[77],"topic.":[78],"In":[79],"this":[80,85],"paper,":[81],"we":[82,267],"demonstrate":[83],"type":[86],"estimation":[88,114],"possible":[90],"and":[91,113,189,297],"characterize":[92],"different":[93],"elicitation":[94,106,196,278],"approaches":[95,107],"guide":[97],"development":[99],"future":[101,301],"systems.":[102],"We":[103],"investigate":[104],"eight":[105],"along":[108],"two":[109],"dimensions:":[110],"annotation":[111],"granularity":[112,117],"perspective.":[115],"Annotation":[116],"varied":[119,149],"annotating":[121],"i)":[122],"\"best\"":[125],"label,":[126],"ii)":[127],"all":[128,135,143,222],"relevant":[129,136,144,223],"labels,":[130,137],"iii)":[131],"ranking":[133],"or":[138,160],"iv)":[139],"real-valued":[140],"weights":[141],"for":[142],"labels.":[145],"Estimation":[146],"perspective":[147],"prompting":[151],"workers":[152,170,188,204,219],"either":[154],"respond":[155],"with":[156,254],"their":[157,255],"own":[158,256],"estimate":[162,308],"answer(s)":[165],"they":[167],"expect":[168],"other":[169],"provide.":[172],"Our":[173,280],"study":[174],"ordinal":[176],"annotations":[177,264],"on":[178,303],"emotional":[180],"valence":[181],"facial":[183],"images":[184],"from":[185],"1,960":[186],"crowd":[187],"found":[190],"that,":[191],"surprisingly,":[192],"most":[194,201,213],"fine-grained":[195],"methods":[197],"were":[198],"not":[199],"accurate,":[202],"despite":[203],"spending":[205],"more":[206,284],"time":[207,239],"provide":[209],"answers.":[210],"Instead,":[211],"approach":[215],"was":[216],"ask":[218],"choose":[221],"classes":[224],"others":[226],"have":[228],"selected.":[229],"This":[230],"resulted":[231],"in":[232,236,261,290],"21.4%":[234],"reduction":[235],"required":[240],"reach":[242],"same":[244],"performance":[245],"as":[246,294],"baseline":[248],"(i.e.,":[249],"selecting":[250],"perspective).":[257],"By":[258],"analyzing":[259],"cases":[260],"which":[262],"finer-grained":[263],"degraded":[265],"performance,":[266],"contribute":[268],"understanding":[272],"trade-offs":[275],"between":[276],"approaches.":[279],"work":[281,302],"makes":[282],"it":[283],"tractable":[285],"large-scale":[291],"tasks":[292],"such":[293],"ML":[295],"training,":[296],"aims":[298],"spark":[300],"techniques":[304],"efficiently":[307],"distributions.":[310]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}