{"id":"https://openalex.org/W2058892254","doi":"https://doi.org/10.1145/2684822.2685301","title":"Leveraging In-Batch Annotation Bias for Crowdsourced Active Learning","display_name":"Leveraging In-Batch Annotation Bias for Crowdsourced Active Learning","publication_year":2015,"publication_date":"2015-01-28","ids":{"openalex":"https://openalex.org/W2058892254","doi":"https://doi.org/10.1145/2684822.2685301","mag":"2058892254"},"language":"en","primary_location":{"id":"doi:10.1145/2684822.2685301","is_oa":true,"landing_page_url":"https://doi.org/10.1145/2684822.2685301","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/2684822.2685301?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eighth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/2684822.2685301?download=true","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011279860","display_name":"Honglei Zhuang","orcid":"https://orcid.org/0000-0001-8134-1509"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Honglei Zhuang","raw_affiliation_strings":["University of Illinois at Urbana-Champaign, Urbana, IL, USA","University of Illinois at Urbana/Champaign, Urbana, IL, USA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana, IL, USA","institution_ids":["https://openalex.org/I157725225"]},{"raw_affiliation_string":"University of Illinois at Urbana/Champaign, Urbana, IL, USA#TAB#","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109204519","display_name":"Joel Young","orcid":null},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joel Young","raw_affiliation_strings":["LinkedIn, Mountain View, CA, USA","[LinkedIn, Mountain View, CA, USA]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LinkedIn, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]},{"raw_affiliation_string":"[LinkedIn, Mountain View, CA, USA]","institution_ids":["https://openalex.org/I1316064682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.1681,"has_fulltext":true,"cited_by_count":32,"citation_normalized_percentile":{"value":0.96597453,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"243","last_page":"252"},"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9970999956130981,"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":0.9923999905586243,"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/annotation","display_name":"Annotation","score":0.908923864364624},{"id":"https://openalex.org/keywords/crowdsourcing","display_name":"Crowdsourcing","score":0.8922785520553589},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7913931608200073},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7108684778213501},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4363346993923187},{"id":"https://openalex.org/keywords/batch-processing","display_name":"Batch processing","score":0.4305366277694702},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39814817905426025},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.391408234834671},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3444805145263672},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.08415335416793823}],"concepts":[{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.908923864364624},{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.8922785520553589},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7913931608200073},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7108684778213501},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4363346993923187},{"id":"https://openalex.org/C172658912","wikidata":"https://www.wikidata.org/wiki/Q661613","display_name":"Batch processing","level":2,"score":0.4305366277694702},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39814817905426025},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.391408234834671},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3444805145263672},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.08415335416793823},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/2684822.2685301","is_oa":true,"landing_page_url":"https://doi.org/10.1145/2684822.2685301","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/2684822.2685301?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eighth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.708.3321","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.708.3321","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://web.engr.illinois.edu/%7Ehzhuang3/files/WSDM15-Zhuang-et-al-Leveraging.pdf","raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/2684822.2685301","is_oa":true,"landing_page_url":"https://doi.org/10.1145/2684822.2685301","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/2684822.2685301?download=true","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eighth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2058892254.pdf","grobid_xml":"https://content.openalex.org/works/W2058892254.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W191327111","https://openalex.org/W1845402413","https://openalex.org/W1967899171","https://openalex.org/W1970381522","https://openalex.org/W1980125555","https://openalex.org/W1991234467","https://openalex.org/W1993202648","https://openalex.org/W2006484294","https://openalex.org/W2010135967","https://openalex.org/W2027953712","https://openalex.org/W2062236692","https://openalex.org/W2071446250","https://openalex.org/W2094607109","https://openalex.org/W2104848109","https://openalex.org/W2114188922","https://openalex.org/W2116664070","https://openalex.org/W2125943921","https://openalex.org/W2134305421","https://openalex.org/W2137795521","https://openalex.org/W2137935418","https://openalex.org/W2141649520","https://openalex.org/W2142518823","https://openalex.org/W2149273804","https://openalex.org/W2168396085","https://openalex.org/W2181558882","https://openalex.org/W2251311344","https://openalex.org/W2330857546","https://openalex.org/W2551217272","https://openalex.org/W2605991684","https://openalex.org/W2903158431","https://openalex.org/W6680113041","https://openalex.org/W6682171051","https://openalex.org/W6684687462","https://openalex.org/W6685936606","https://openalex.org/W6736502011","https://openalex.org/W6768433217"],"related_works":["https://openalex.org/W2463235902","https://openalex.org/W2109021302","https://openalex.org/W4288474950","https://openalex.org/W3048081621","https://openalex.org/W2058892254","https://openalex.org/W86096423","https://openalex.org/W3153922349","https://openalex.org/W151193258","https://openalex.org/W1942954136","https://openalex.org/W2947809439"],"abstract_inverted_index":{"Data":[0],"annotation":[1,52,77,93,100,109,140],"bias":[2,35,53,105,165],"is":[3,23],"found":[4],"in":[5,26,82,114,132],"many":[6],"situations.":[7],"Often":[8],"it":[9,22,162],"can":[10],"be":[11,31],"ignored":[12],"as":[13],"just":[14],"another":[15],"component":[16],"of":[17,56,75,111,129],"the":[18,73,83,98,104,126,138,152],"noise":[19],"floor.":[20],"However,":[21],"especially":[24],"prevalent":[25],"crowdsourcing":[27,108,157],"tasks":[28],"and":[29,102,159],"must":[30],"actively":[32],"managed.":[33],"Annotation":[34],"on":[36,54,154],"single":[37],"data":[38,46,58,80,130],"items":[39,59,81,131],"has":[40,64],"been":[41,66],"studied":[42],"with":[43],"regard":[44],"to":[45,62,95,121,142],"difficulty,":[47],"annotator":[48],"bias,":[49,101],"etc.,":[50],"while":[51],"batches":[55],"multiple":[57],"simultaneously":[60],"presented":[61],"annotators":[63,119],"not":[65],"studied.":[67],"In":[68],"this":[69],"paper,":[70],"we":[71],"verify":[72],"existence":[74],"\"in-batch":[76],"bias\"":[78],"between":[79],"same":[84],"batch.":[85],"We":[86,116,135,150],"propose":[87,143],"a":[88,107,144,155],"factor":[89],"graph":[90],"based":[91],"batch":[92,128,139,146],"model":[94,141],"quantitatively":[96],"capture":[97],"in-batch":[99,164],"measure":[103],"during":[106],"process":[110],"inappropriate":[112],"comments":[113],"LinkedIn.":[115],"discover":[117],"that":[118,161],"tend":[120],"make":[122],"polarized":[123],"annotations":[124],"for":[125],"entire":[127],"our":[133],"task.":[134],"further":[136],"leverage":[137],"novel":[145],"active":[147],"learning":[148],"algorithm.":[149],"test":[151],"algorithm":[153],"real":[156],"platform":[158],"find":[160],"outperforms":[163],"na\u00efve":[166],"algorithms.":[167]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
