{"id":"https://openalex.org/W4224324956","doi":"https://doi.org/10.1145/3485447.3511968","title":"Cross-modal Ambiguity Learning for Multimodal Fake News Detection","display_name":"Cross-modal Ambiguity Learning for Multimodal Fake News Detection","publication_year":2022,"publication_date":"2022-04-25","ids":{"openalex":"https://openalex.org/W4224324956","doi":"https://doi.org/10.1145/3485447.3511968"},"language":"en","primary_location":{"id":"doi:10.1145/3485447.3511968","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3485447.3511968","pdf_url":null,"source":{"id":"https://openalex.org/S4363608783","display_name":"Proceedings of the ACM Web Conference 2022","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2022","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100725680","display_name":"Yixuan Chen","orcid":"https://orcid.org/0000-0002-3424-7605"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yixuan Chen","raw_affiliation_strings":["School of Computer Science, Fudan University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100440903","display_name":"Dongsheng Li","orcid":"https://orcid.org/0000-0001-9743-2034"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongsheng Li","raw_affiliation_strings":["Microsoft Research Asia, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100364191","display_name":"Peng Zhang","orcid":"https://orcid.org/0000-0002-9109-4625"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Zhang","raw_affiliation_strings":["School of Computer Science, Fudan University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103253497","display_name":"Jie Sui","orcid":"https://orcid.org/0000-0003-0100-8197"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Sui","raw_affiliation_strings":["University of Chinese Academy of Sciences, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089831843","display_name":"Qin Lv","orcid":"https://orcid.org/0000-0002-9437-1376"},"institutions":[{"id":"https://openalex.org/I188538660","display_name":"University of Colorado Boulder","ror":"https://ror.org/02ttsq026","country_code":"US","type":"education","lineage":["https://openalex.org/I188538660"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qin Lv","raw_affiliation_strings":["University of Colorado Boulder, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Colorado Boulder, USA","institution_ids":["https://openalex.org/I188538660"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004237040","display_name":"Tun Lu","orcid":"https://orcid.org/0000-0002-6633-4826"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lu Tun","raw_affiliation_strings":["School of Computer Science, Fudan University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004722925","display_name":"Li Shang","orcid":"https://orcid.org/0000-0003-3944-7531"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Shang","raw_affiliation_strings":["School of Computer Science, Fudan University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Fudan University, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":324,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2897","last_page":"2905"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":1.0,"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"}},{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":0.989300012588501,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9768999814987183,"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/ambiguity","display_name":"Ambiguity","score":0.8392961025238037},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.8247244358062744},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.660649299621582},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48328354954719543},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.44919630885124207},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3415314555168152}],"concepts":[{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.8392961025238037},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.8247244358062744},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.660649299621582},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48328354954719543},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.44919630885124207},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3415314555168152},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3485447.3511968","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3485447.3511968","pdf_url":null,"source":{"id":"https://openalex.org/S4363608783","display_name":"Proceedings of the ACM Web Conference 2022","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Web Conference 2022","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G219854528","display_name":null,"funder_award_id":"61932007, 61902075, 61572459","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W618024573","https://openalex.org/W1796766288","https://openalex.org/W1966811077","https://openalex.org/W2086234878","https://openalex.org/W2163922914","https://openalex.org/W2194775991","https://openalex.org/W2531862055","https://openalex.org/W2588172982","https://openalex.org/W2595551253","https://openalex.org/W2610676001","https://openalex.org/W2619383789","https://openalex.org/W2742330194","https://openalex.org/W2766462585","https://openalex.org/W2773666902","https://openalex.org/W2809476703","https://openalex.org/W2965096309","https://openalex.org/W2982137384","https://openalex.org/W2983067441","https://openalex.org/W2997128522","https://openalex.org/W3003961771","https://openalex.org/W3022924198","https://openalex.org/W3034531060","https://openalex.org/W3093115118","https://openalex.org/W3099948566","https://openalex.org/W3125491592","https://openalex.org/W3152907744","https://openalex.org/W3155131971","https://openalex.org/W3155609342","https://openalex.org/W3156800454","https://openalex.org/W3157731560","https://openalex.org/W3163153129","https://openalex.org/W4233708085","https://openalex.org/W4254884593"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4313488044","https://openalex.org/W3209574120","https://openalex.org/W4301143707","https://openalex.org/W4312192474"],"abstract_inverted_index":{"Cross-modal":[0],"learning":[1,65,105],"is":[2,149,159],"essential":[3],"to":[4,11,50,90,107,120,153],"enable":[5],"accurate":[6],"fake":[7,25,53,78,127,176],"news":[8,26,54,79,128,177],"detection":[9,27,80,129,178],"due":[10],"the":[12,30,62,92,109,122],"fast-growing":[13],"multimodal":[14,24,52,77],"contents":[15],"in":[16,29],"online":[17],"social":[18],"communities.":[19],"A":[20],"fundamental":[21],"challenge":[22],"of":[23,84],"lies":[28],"inherent":[31],"ambiguity":[32,64,104,110,148,158],"across":[33],"different":[34,112],"content":[35],"modalities,":[36,113],"i.e.,":[37,141],"decisions":[38],"made":[39],"from":[40,67],"unimodalities":[41],"may":[42,48],"disagree":[43],"with":[44],"each":[45],"other,":[46],"which":[47],"lead":[49],"inferior":[51],"detection.":[55],"To":[56],"address":[57],"this":[58],"issue,":[59],"we":[60],"formulate":[61],"cross-modal":[63,87,103,117,123,139,147,154,157],"problem":[66],"an":[68,75],"information-theoretic":[69],"perspective":[70],"and":[71,114,133,138,151,169,182],"propose":[72],"CAFE":[73,82,125,173],"\u2014":[74],"ambiguity-aware":[76],"method.":[81],"consists":[83],"1)":[85],"a":[86,97,102,116],"alignment":[88],"module":[89,106,119],"transform":[91],"heterogeneous":[93],"unimodality":[94],"features":[95,137,145],"into":[96],"shared":[98],"semantic":[99],"space,":[100],"2)":[101],"estimate":[108],"between":[111],"3)":[115],"fusion":[118],"capture":[121],"correlations.":[124],"improves":[126],"accuracy":[130],"by":[131,180],"judiciously":[132],"adaptively":[134],"aggregating":[135],"unimodal":[136,144],"correlations,":[140],"relying":[142],"on":[143,163,184],"when":[146,156],"weak":[150],"referring":[152],"correlations":[155],"strong.":[160],"Experimental":[161],"studies":[162],"two":[164],"widely":[165],"used":[166],"datasets":[167],"(Twitter":[168],"Weibo)":[170],"demonstrate":[171],"that":[172],"outperforms":[174],"state-of-the-art":[175],"methods":[179],"2.2-18.9%":[181],"1.7-11.4%":[183],"accuracy,":[185],"respectively.":[186]},"counts_by_year":[{"year":2026,"cited_by_count":48},{"year":2025,"cited_by_count":135},{"year":2024,"cited_by_count":88},{"year":2023,"cited_by_count":51},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-18T07:39:51.176621","created_date":"2025-10-10T00:00:00"}