{"id":"https://openalex.org/W2962960080","doi":"https://doi.org/10.1109/bigdata.2016.7840894","title":"Pricing the woman card: Gender politics between hillary clinton and donald trump","display_name":"Pricing the woman card: Gender politics between hillary clinton and donald trump","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2962960080","doi":"https://doi.org/10.1109/bigdata.2016.7840894","mag":"2962960080"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2016.7840894","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2016.7840894","pdf_url":null,"source":null,"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"},"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/A5100616896","display_name":"Yu Wang","orcid":"https://orcid.org/0000-0002-9517-9263"},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yu Wang","raw_affiliation_strings":["Department of Political Science, University of Rochester, Rochester, NY, United States"],"affiliations":[{"raw_affiliation_string":"Department of Political Science, University of Rochester, Rochester, NY, United States","institution_ids":["https://openalex.org/I5388228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086776932","display_name":"Yang Feng","orcid":"https://orcid.org/0000-0001-5338-8967"},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yang Feng","raw_affiliation_strings":["Department of Political Science, University of Rochester, Rochester, NY, United States"],"affiliations":[{"raw_affiliation_string":"Department of Political Science, University of Rochester, Rochester, NY, United States","institution_ids":["https://openalex.org/I5388228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055469774","display_name":"Jiebo Luo","orcid":"https://orcid.org/0000-0002-4516-9729"},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiebo Luo","raw_affiliation_strings":["Department of Political Science, University of Rochester, Rochester, NY, United States"],"affiliations":[{"raw_affiliation_string":"Department of Political Science, University of Rochester, Rochester, NY, United States","institution_ids":["https://openalex.org/I5388228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008983384","display_name":"Xiyang Zhang","orcid":"https://orcid.org/0000-0001-7856-5202"},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiyang Zhang","raw_affiliation_strings":["Department of Political Science, University of Rochester, Rochester, NY, United States"],"affiliations":[{"raw_affiliation_string":"Department of Political Science, University of Rochester, Rochester, NY, United States","institution_ids":["https://openalex.org/I5388228"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100616896"],"corresponding_institution_ids":["https://openalex.org/I5388228"],"apc_list":null,"apc_paid":null,"fwci":2.1424,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.91281829,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2541","last_page":"2544"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12380","display_name":"Authorship Attribution and Profiling","score":0.9943000078201294,"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":0.9943000078201294,"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/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":0.9865999817848206,"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/T13718","display_name":"Media Influence and Politics","score":0.9747999906539917,"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/politics","display_name":"Politics","score":0.7375367879867554},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6046419739723206},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4144821763038635},{"id":"https://openalex.org/keywords/political-science","display_name":"Political science","score":0.3832305073738098},{"id":"https://openalex.org/keywords/advertising","display_name":"Advertising","score":0.3797381818294525},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.3547776937484741},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.25662708282470703},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.21261784434318542},{"id":"https://openalex.org/keywords/law","display_name":"Law","score":0.11189818382263184}],"concepts":[{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.7375367879867554},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6046419739723206},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4144821763038635},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.3832305073738098},{"id":"https://openalex.org/C112698675","wikidata":"https://www.wikidata.org/wiki/Q37038","display_name":"Advertising","level":1,"score":0.3797381818294525},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3547776937484741},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25662708282470703},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.21261784434318542},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.11189818382263184}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2016.7840894","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2016.7840894","pdf_url":null,"source":null,"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":[{"display_name":"Gender equality","score":0.699999988079071,"id":"https://metadata.un.org/sdg/5"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W9292421","https://openalex.org/W2022422561","https://openalex.org/W2023161323","https://openalex.org/W2122369144","https://openalex.org/W2122628122","https://openalex.org/W2135148528","https://openalex.org/W2963036438","https://openalex.org/W2963504255","https://openalex.org/W2963817807","https://openalex.org/W2964276636","https://openalex.org/W6600376888","https://openalex.org/W6697497428","https://openalex.org/W6702667248"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W4293226380","https://openalex.org/W2931662336","https://openalex.org/W2077865380","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4401768695","https://openalex.org/W2765597752","https://openalex.org/W2134894512"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,44,66],"introduce":[4],"computer":[5],"vision":[6],"to":[7,18,72,89,107,113],"the":[8,20,23,39,47,51,59,74,77,90,98,123],"study":[9,82,95],"of":[10,22,38,50,76,126],"gender":[11,75,124],"politics":[12],"and":[13,30,61,79,81,116],"present":[14],"a":[15,35,68],"data-driven":[16],"method":[17],"measure":[19],"impact":[21],"`woman":[24,91,99],"card'":[25,92,100],"exchange":[26,60],"between":[27],"Hillary":[28,109],"Clinton":[29],"Donald":[31],"Trump.":[32],"Building":[33],"from":[34],"unique":[36],"dataset":[37],"two":[40,52],"candidates'":[41,53],"Twitter":[42,54],"followers,":[43],"first":[45],"examine":[46],"transition":[48],"dynamics":[49],"followers":[55,78],"one":[56,62],"week":[57,63],"before":[58],"after.":[64],"Then":[65],"train":[67],"convolutional":[69],"neural":[70],"network":[71],"classify":[73],"unfollowers,":[80],"how":[83],"women":[84,104],"in":[85],"particular":[86],"are":[87],"reacting":[88],"exchange.":[93],"Our":[94],"suggests":[96],"that":[97,117],"comment":[101],"has":[102,119],"made":[103],"more":[105],"likely":[106,112],"follow":[108],"Clinton,":[110],"less":[111],"unfollow":[114],"her":[115],"it":[118],"apparently":[120],"not":[121],"affected":[122],"composition":[125],"Trump":[127],"followers.":[128]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
