{"id":"https://openalex.org/W4399423024","doi":"https://doi.org/10.1145/3652583.3658118","title":"Intra and Inter-modality Incongruity Modeling and Adversarial Contrastive Learning for Multimodal Fake News Detection","display_name":"Intra and Inter-modality Incongruity Modeling and Adversarial Contrastive Learning for Multimodal Fake News Detection","publication_year":2024,"publication_date":"2024-05-30","ids":{"openalex":"https://openalex.org/W4399423024","doi":"https://doi.org/10.1145/3652583.3658118"},"language":"en","primary_location":{"id":"doi:10.1145/3652583.3658118","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3652583.3658118","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3652583.3658118","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 International Conference on Multimedia Retrieval","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/10.1145/3652583.3658118","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059919541","display_name":"Siqi Wei","orcid":"https://orcid.org/0000-0002-5824-9445"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Siqi Wei","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-5824-9445","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101432128","display_name":"Bin Wu","orcid":"https://orcid.org/0000-0002-7112-126X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Wu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7112-126X","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5059919541"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":3.2233,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.91892563,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"666","last_page":"674"},"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":0.9836999773979187,"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.970300018787384,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/computer-science","display_name":"Computer science","score":0.7930536866188049},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7904565334320068},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.6140025854110718},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6086881160736084},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5872151255607605},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.553003191947937},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4711659848690033},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4610620439052582},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4547225832939148},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.41647353768348694},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.41564011573791504},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.11935362219810486}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7930536866188049},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7904565334320068},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.6140025854110718},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6086881160736084},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5872151255607605},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.553003191947937},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4711659848690033},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4610620439052582},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4547225832939148},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.41647353768348694},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.41564011573791504},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.11935362219810486},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3652583.3658118","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3652583.3658118","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3652583.3658118","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3652583.3658118","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3652583.3658118","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3652583.3658118","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3915844376","display_name":null,"funder_award_id":"61972047","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6687479257","display_name":null,"funder_award_id":"U1936220","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6803359698","display_name":null,"funder_award_id":"61972047,62372060","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4399423024.pdf","grobid_xml":"https://content.openalex.org/works/W4399423024.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W2742330194","https://openalex.org/W2766462585","https://openalex.org/W2773666902","https://openalex.org/W2809476703","https://openalex.org/W2907492528","https://openalex.org/W2912305564","https://openalex.org/W2912642460","https://openalex.org/W2963568027","https://openalex.org/W2994071599","https://openalex.org/W3016549807","https://openalex.org/W3022924198","https://openalex.org/W3031781733","https://openalex.org/W3037666784","https://openalex.org/W3106237638","https://openalex.org/W3156558703","https://openalex.org/W3157731560","https://openalex.org/W3176713826","https://openalex.org/W3197618246","https://openalex.org/W4210738268","https://openalex.org/W4283211888","https://openalex.org/W4307907065","https://openalex.org/W4322576826","https://openalex.org/W4361991106","https://openalex.org/W4382246105","https://openalex.org/W4386172480"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2482350142","https://openalex.org/W4246396837","https://openalex.org/W3126451824","https://openalex.org/W2965546495","https://openalex.org/W1561927205","https://openalex.org/W3191453585","https://openalex.org/W4297672492","https://openalex.org/W4389116644","https://openalex.org/W4310988119"],"abstract_inverted_index":{"Multimodal":[0],"fake":[1,70,97,118,160],"news":[2,44,106,119,161,178],"detection":[3,94,198],"(FND)":[4],"is":[5],"significant":[6],"in":[7,42,104],"safeguarding":[8],"network":[9],"security":[10],"and":[11,26,115,140,144,163,186,204,209],"societal":[12],"safety.":[13],"Most":[14],"existing":[15,195],"studies":[16,36],"only":[17],"focus":[18],"on":[19,206],"common":[20,139],"semantic":[21,40,102],"features":[22,41],"between":[23,51],"different":[24],"modalities":[25],"utilize":[27],"simple":[28,57],"cross-entropy":[29,58],"loss":[30,59],"for":[31,92,184],"model":[32,64,174],"training.":[33],"However,":[34],"these":[35],"overlook":[37],"the":[38,54,63,74,93,117,173,181],"incongruent":[39,141],"multimodal":[43,96,105,113],"data,":[45],"which":[46],"can":[47],"arise":[48],"within":[49,143],"or":[50],"modalities.":[52,146],"Moreover,":[53],"utilization":[55],"of":[56,95,201],"may":[60],"not":[61],"provide":[62],"with":[65,87,197],"robustness":[66],"against":[67],"well-designed":[68],"forged":[69],"news.":[71,98],"To":[72],"address":[73],"above":[75],"issues,":[76],"we":[77],"propose":[78],"a":[79,110,123,130],"novel":[80],"approach":[81],"named":[82],"Signed":[83],"Attention-based":[84],"Graph":[85],"Transformer":[86],"Adversarial":[88],"Contrastive":[89],"Learning":[90],"(SAGT-ACL)":[91],"SAGT-ACL":[99,128,148,193],"models":[100],"fine-grained":[101],"associations":[103],"articles":[107],"by":[108,159],"constructing":[109],"fully":[111],"connected":[112],"graph":[114,124,133],"reframes":[116],"classification":[120,125],"task":[121,170],"as":[122],"problem.":[126],"Additionally,":[127],"incorporates":[129],"signed":[131],"attention-based":[132],"transformer":[134],"module":[135],"to":[136,155,171],"identify":[137],"both":[138],"semantics":[142],"across":[145],"Finally,":[147],"proposes":[149],"an":[150,165],"adversarial":[151,167,182],"data":[152],"augmentation":[153],"mechanism":[154],"simulate":[156],"malicious":[157],"forgeries":[158],"creators":[162],"designs":[164],"auxiliary":[166],"contrastive":[168],"learning":[169],"help":[172],"learn":[175],"more":[176],"discriminative":[177],"representations":[179],"from":[180],"samples":[183],"robust":[185],"effective":[187],"detection.":[188],"Extensive":[189],"experiments":[190],"demonstrate":[191],"that":[192],"outperforms":[194],"methods,":[196],"accuracy":[199],"improvements":[200],"4.95%,":[202],"6.01%,":[203],"5.68%":[205],"Weibo,":[207],"Twitter,":[208],"Gossipcop":[210],"datasets,":[211],"respectively.":[212]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
