{"id":"https://openalex.org/W2251438779","doi":"https://doi.org/10.18653/v1/w15-2814","title":"A Weighted Combination of Text and Image Classifiers for User Gender Inference","display_name":"A Weighted Combination of Text and Image Classifiers for User Gender Inference","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W2251438779","doi":"https://doi.org/10.18653/v1/w15-2814","mag":"2251438779"},"language":"en","primary_location":{"id":"doi:10.18653/v1/w15-2814","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w15-2814","pdf_url":"https://www.aclweb.org/anthology/W15-2814.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Fourth Workshop on Vision and Language","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/W15-2814.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109512832","display_name":"Tomoki Taniguchi","orcid":null},"institutions":[{"id":"https://openalex.org/I15009632","display_name":"Fuji Xerox (Japan)","ror":"https://ror.org/02w528w58","country_code":"JP","type":"company","lineage":["https://openalex.org/I15009632"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomoki Taniguchi","raw_affiliation_strings":["Fuji Xerox Co., Ltd. , 6-1, Minatomirai, Nishiku, Yokohama-shi, Kanagawa, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fuji Xerox Co., Ltd. , 6-1, Minatomirai, Nishiku, Yokohama-shi, Kanagawa, Japan","institution_ids":["https://openalex.org/I15009632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103818873","display_name":"Shigeyuki Sakaki","orcid":null},"institutions":[{"id":"https://openalex.org/I15009632","display_name":"Fuji Xerox (Japan)","ror":"https://ror.org/02w528w58","country_code":"JP","type":"company","lineage":["https://openalex.org/I15009632"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shigeyuki Sakaki","raw_affiliation_strings":["Fuji Xerox Co., Ltd. , 6-1, Minatomirai, Nishiku, Yokohama-shi, Kanagawa, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fuji Xerox Co., Ltd. , 6-1, Minatomirai, Nishiku, Yokohama-shi, Kanagawa, Japan","institution_ids":["https://openalex.org/I15009632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040343966","display_name":"Ryosuke Shigenaka","orcid":null},"institutions":[{"id":"https://openalex.org/I15009632","display_name":"Fuji Xerox (Japan)","ror":"https://ror.org/02w528w58","country_code":"JP","type":"company","lineage":["https://openalex.org/I15009632"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryosuke Shigenaka","raw_affiliation_strings":["Fuji Xerox Co., Ltd. , 6-1, Minatomirai, Nishiku, Yokohama-shi, Kanagawa, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fuji Xerox Co., Ltd. , 6-1, Minatomirai, Nishiku, Yokohama-shi, Kanagawa, Japan","institution_ids":["https://openalex.org/I15009632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055617670","display_name":"Yukihiro Tsuboshita","orcid":null},"institutions":[{"id":"https://openalex.org/I15009632","display_name":"Fuji Xerox (Japan)","ror":"https://ror.org/02w528w58","country_code":"JP","type":"company","lineage":["https://openalex.org/I15009632"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yukihiro Tsuboshita","raw_affiliation_strings":["Fuji Xerox Co., Ltd. , 6-1, Minatomirai, Nishiku, Yokohama-shi, Kanagawa, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fuji Xerox Co., Ltd. , 6-1, Minatomirai, Nishiku, Yokohama-shi, Kanagawa, Japan","institution_ids":["https://openalex.org/I15009632"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037841139","display_name":"Tomoko Ohkuma","orcid":"https://orcid.org/0000-0002-5078-4814"},"institutions":[{"id":"https://openalex.org/I15009632","display_name":"Fuji Xerox (Japan)","ror":"https://ror.org/02w528w58","country_code":"JP","type":"company","lineage":["https://openalex.org/I15009632"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tomoko Ohkuma","raw_affiliation_strings":["Fuji Xerox Co., Ltd. , 6-1, Minatomirai, Nishiku, Yokohama-shi, Kanagawa, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fuji Xerox Co., Ltd. , 6-1, Minatomirai, Nishiku, Yokohama-shi, Kanagawa, Japan","institution_ids":["https://openalex.org/I15009632"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.3355,"has_fulltext":true,"cited_by_count":10,"citation_normalized_percentile":{"value":0.86885279,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"87","last_page":"93"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12380","display_name":"Authorship Attribution and Profiling","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T12380","display_name":"Authorship Attribution and Profiling","score":0.9997000098228455,"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/T11644","display_name":"Spam and Phishing Detection","score":0.9898999929428101,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12970","display_name":"Names, Identity, and Discrimination Research","score":0.9775000214576721,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7649673819541931},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.763885498046875},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.740851640701294},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.6941200494766235},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6168298721313477},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.5788750648498535},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47074586153030396},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3739720582962036},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.35912245512008667}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7649673819541931},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.763885498046875},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.740851640701294},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.6941200494766235},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6168298721313477},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5788750648498535},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47074586153030396},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3739720582962036},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35912245512008667}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/w15-2814","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w15-2814","pdf_url":"https://www.aclweb.org/anthology/W15-2814.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Fourth Workshop on Vision and Language","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/w15-2814","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w15-2814","pdf_url":"https://www.aclweb.org/anthology/W15-2814.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Fourth Workshop on Vision and Language","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.46000000834465027},{"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5","score":0.4099999964237213}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2251438779.pdf","grobid_xml":"https://content.openalex.org/works/W2251438779.grobid-xml"},"referenced_works_count":9,"referenced_works":["https://openalex.org/W9292421","https://openalex.org/W2092577289","https://openalex.org/W2108781466","https://openalex.org/W2118585731","https://openalex.org/W2153635508","https://openalex.org/W2163605009","https://openalex.org/W2250394708","https://openalex.org/W2293147617","https://openalex.org/W3120421331"],"related_works":["https://openalex.org/W2329500892","https://openalex.org/W28991112","https://openalex.org/W2370726991","https://openalex.org/W2369710579","https://openalex.org/W4327728159","https://openalex.org/W4394266730","https://openalex.org/W1990856605","https://openalex.org/W2053783616","https://openalex.org/W2545348020","https://openalex.org/W2912751582"],"abstract_inverted_index":{"Demographic":[0],"attribute":[1],"inference":[2,25],"of":[3,56,64],"social":[4],"networking":[5],"service":[6],"(SNS)":[7],"users":[8],"is":[9,67],"a":[10,37,54,57,72],"valuable":[11],"application":[12],"for":[13,16],"marketing":[14],"and":[15,32,43,75],"targeting":[17],"advertisements.":[18],"Several":[19],"studies":[20],"have":[21],"examined":[22],"Twitter-user":[23],"gender":[24,88],"in":[26],"natural":[27],"language":[28],"processing,":[29],"image":[30,44,78],"recognition,":[31],"other":[33],"research":[34],"domains.":[35],"Reportedly,":[36],"combined":[38,81],"approach":[39,99],"using":[40,89],"text":[41,73],"data":[42,45,49],"outperforms":[46],"an":[47,77,102],"individual":[48],"approach.":[50,60],"This":[51],"paper":[52],"presents":[53],"proposal":[55],"novel":[58],"hybrid":[59],"A":[61],"salient":[62],"benefit":[63],"our":[65,98],"system":[66],"that":[68,97],"features":[69],"provided":[70],"from":[71,76],"classifier":[74,79],"are":[80],"appropriately":[82],"to":[83],"infer":[84],"male":[85],"or":[86],"female":[87],"logistic":[90],"regression.":[91],"The":[92],"experimentally":[93],"obtained":[94],"results":[95],"demonstrate":[96],"markedly":[100],"improves":[101],"existing":[103],"combination-based":[104],"method.":[105]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
