{"id":"https://openalex.org/W4224318954","doi":"https://doi.org/10.1145/3485447.3511999","title":"Rumor Detection on Social Media with Graph Adversarial Contrastive Learning","display_name":"Rumor Detection on Social Media with Graph Adversarial Contrastive Learning","publication_year":2022,"publication_date":"2022-04-25","ids":{"openalex":"https://openalex.org/W4224318954","doi":"https://doi.org/10.1145/3485447.3511999"},"language":"en","primary_location":{"id":"doi:10.1145/3485447.3511999","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3485447.3511999","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":"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/A5084964975","display_name":"Tiening Sun","orcid":"https://orcid.org/0000-0002-2500-760X"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tiening Sun","raw_affiliation_strings":["Soochow University, China"],"affiliations":[{"raw_affiliation_string":"Soochow University, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042912655","display_name":"Qian Zhong","orcid":"https://orcid.org/0000-0001-7651-7872"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhong Qian","raw_affiliation_strings":["Soochow University, China"],"affiliations":[{"raw_affiliation_string":"Soochow University, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048781385","display_name":"Sujun Dong","orcid":null},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sujun Dong","raw_affiliation_strings":["Soochow University, China"],"affiliations":[{"raw_affiliation_string":"Soochow University, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100695549","display_name":"Peifeng Li","orcid":"https://orcid.org/0000-0003-4850-3128"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peifeng Li","raw_affiliation_strings":["Soochow University, China"],"affiliations":[{"raw_affiliation_string":"Soochow University, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102065469","display_name":"Qiaoming Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiaoming Zhu","raw_affiliation_strings":["Soochow University, China"],"affiliations":[{"raw_affiliation_string":"Soochow University, China","institution_ids":["https://openalex.org/I3923682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5084964975"],"corresponding_institution_ids":["https://openalex.org/I3923682"],"apc_list":null,"apc_paid":null,"fwci":27.5554,"has_fulltext":false,"cited_by_count":98,"citation_normalized_percentile":{"value":0.99853961,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2789","last_page":"2797"},"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":0.9901000261306763,"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":0.9890000224113464,"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/rumor","display_name":"Rumor","score":0.9000875949859619},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.8234288692474365},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7009595632553101},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.5881614089012146},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4874408543109894},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4185277223587036},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3357090950012207},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.19399353861808777},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1882401406764984},{"id":"https://openalex.org/keywords/political-science","display_name":"Political science","score":0.09792429208755493}],"concepts":[{"id":"https://openalex.org/C2780469804","wikidata":"https://www.wikidata.org/wiki/Q878352","display_name":"Rumor","level":2,"score":0.9000875949859619},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8234288692474365},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7009595632553101},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.5881614089012146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4874408543109894},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4185277223587036},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3357090950012207},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.19399353861808777},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1882401406764984},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.09792429208755493},{"id":"https://openalex.org/C39549134","wikidata":"https://www.wikidata.org/wiki/Q133080","display_name":"Public relations","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3485447.3511999","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3485447.3511999","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":[{"display_name":"Reduced inequalities","score":0.5199999809265137,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1975594555","https://openalex.org/W2737907513","https://openalex.org/W2741930413","https://openalex.org/W2742144412","https://openalex.org/W2766462585","https://openalex.org/W2798787718","https://openalex.org/W2798966390","https://openalex.org/W2905513100","https://openalex.org/W2912078280","https://openalex.org/W2951288507","https://openalex.org/W2955015412","https://openalex.org/W2980708516","https://openalex.org/W2997128522","https://openalex.org/W3004200020","https://openalex.org/W3034531060","https://openalex.org/W3036446966","https://openalex.org/W3095602948","https://openalex.org/W3095746859","https://openalex.org/W3099152386","https://openalex.org/W3104186312","https://openalex.org/W3154503084","https://openalex.org/W3171007011","https://openalex.org/W3191441002","https://openalex.org/W6784694379"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2372853429","https://openalex.org/W2793319716","https://openalex.org/W3110636174","https://openalex.org/W4210503589","https://openalex.org/W2908713064","https://openalex.org/W2810386322","https://openalex.org/W2801267666","https://openalex.org/W2188429085","https://openalex.org/W2886888575"],"abstract_inverted_index":{"Rumors":[0],"spread":[1],"through":[2],"the":[3,21,29,70,79,111,119,130,137,174],"Internet,":[4],"especially":[5],"on":[6,182],"Twitter,":[7],"have":[8],"harmed":[9],"social":[10],"stability":[11],"and":[12,66,74,133,178],"residents\u2019":[13],"daily":[14],"lives.":[15],"Recently,":[16],"in":[17,69,169],"addition":[18],"to":[19,63,105,148,155,172],"utilizing":[20],"text":[22],"features":[23,44],"of":[24,32,72,118,129],"posts":[25],"for":[26,122,152],"rumor":[27,33],"detection,":[28],"structural":[30],"information":[31],"propagation":[34],"trees":[35],"has":[36],"also":[37,163],"been":[38],"valued.":[39],"Most":[40],"rumors":[41],"with":[42],"salient":[43],"can":[45],"be":[46],"quickly":[47],"locked":[48],"by":[49,53],"graph":[50],"models":[51,60],"dominated":[52],"cross":[54],"entropy":[55],"loss.":[56],"However,":[57],"these":[58,107],"conventional":[59],"may":[61],"lead":[62],"poor":[64],"generalization,":[65],"lack":[67],"robustness":[68],"face":[71],"noise":[73],"adversarial":[75,160],"rumors,":[76],"or":[77,88],"even":[78],"conversational":[80,127],"structures":[81],"that":[82,188],"is":[83,114,146],"deliberately":[84],"perturbed":[85],"(e.g.,":[86],"adding":[87],"deleting":[89],"some":[90],"comments).":[91],"In":[92],"this":[93],"paper,":[94],"we":[95],"propose":[96],"a":[97],"novel":[98],"Graph":[99],"Adversarial":[100,141],"Contrastive":[101],"Learning":[102],"(GACL)":[103],"method":[104,191],"fight":[106],"complex":[108],"cases,":[109],"where":[110],"contrastive":[112,170],"learning":[113,171],"introduced":[115],"as":[116,165],"part":[117],"loss":[120],"function":[121],"explicitly":[123],"perceiving":[124],"differences":[125],"between":[126],"threads":[128],"same":[131,138],"class":[132],"different":[134],"classes.":[135],"At":[136],"time,":[139],"an":[140],"Feature":[142],"Transformation":[143],"(AFT)":[144],"module":[145],"designed":[147],"produce":[149],"conflicting":[150],"samples":[151,161,168],"pressurizing":[153],"model":[154,175],"mine":[156],"event-invariant":[157],"features.":[158],"These":[159],"are":[162],"used":[164],"hard":[166],"negative":[167],"make":[173],"more":[176],"robust":[177],"effective.":[179],"Experimental":[180],"results":[181,194],"three":[183],"public":[184],"benchmark":[185],"datasets":[186],"prove":[187],"our":[189],"GACL":[190],"achieves":[192],"better":[193],"than":[195],"other":[196],"state-of-the-art":[197],"models.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":32},{"year":2024,"cited_by_count":35},{"year":2023,"cited_by_count":25},{"year":2022,"cited_by_count":2}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}