{"id":"https://openalex.org/W7152581396","doi":"https://doi.org/10.48550/arxiv.2604.06666","title":"A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM","display_name":"A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM","publication_year":2026,"publication_date":"2026-04-08","ids":{"openalex":"https://openalex.org/W7152581396","doi":"https://doi.org/10.48550/arxiv.2604.06666"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.06666","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06666","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.06666","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133245227","display_name":"Bo Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Bo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133243757","display_name":"Jing Ma","orcid":"https://orcid.org/0009-0008-9616-5067"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Jing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133254163","display_name":"Hongzhan Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Hongzhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133309026","display_name":"Zhiwei Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Zhiwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133294998","display_name":"Ruichao Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Ruichao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133260513","display_name":"Yuan Tian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian, Yuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133257542","display_name":"Yi Chang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chang, Yi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":0.9397000074386597,"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":0.9397000074386597,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.012400000356137753,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.007400000002235174,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/fake-news","display_name":"Fake news","score":0.8528000116348267},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6848999857902527},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.5501000285148621},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.49939998984336853},{"id":"https://openalex.org/keywords/dependency-graph","display_name":"Dependency graph","score":0.45570001006126404},{"id":"https://openalex.org/keywords/journalism","display_name":"Journalism","score":0.4226999878883362}],"concepts":[{"id":"https://openalex.org/C2779756789","wikidata":"https://www.wikidata.org/wiki/Q28549308","display_name":"Fake news","level":2,"score":0.8528000116348267},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8021000027656555},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6848999857902527},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.5501000285148621},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.49939998984336853},{"id":"https://openalex.org/C16311509","wikidata":"https://www.wikidata.org/wiki/Q4148050","display_name":"Dependency graph","level":3,"score":0.45570001006126404},{"id":"https://openalex.org/C119513131","wikidata":"https://www.wikidata.org/wiki/Q11030","display_name":"Journalism","level":2,"score":0.4226999878883362},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.41029998660087585},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4020000100135803},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.387800008058548},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3732999861240387},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.3416000008583069},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.30489999055862427},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.29109999537467957},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2827000021934509},{"id":"https://openalex.org/C59577422","wikidata":"https://www.wikidata.org/wiki/Q10265143","display_name":"False accusation","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C140547941","wikidata":"https://www.wikidata.org/wiki/Q7797194","display_name":"Threat model","level":2,"score":0.26669999957084656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.06666","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06666","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.06666","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06666","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.6046987771987915}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Explainable":[0],"fake":[1,57],"news":[2,10,58,108],"detection":[3,44,59,174],"aims":[4],"to":[5,71,128,147,157],"assess":[6,148],"the":[7,73,107,123,145,149,176],"veracity":[8,173],"of":[9,68,178],"claims":[11],"while":[12],"providing":[13],"human-friendly":[14],"explanations.":[15,135,180],"Existing":[16],"methods":[17],"incorporating":[18],"investigative":[19],"journalism":[20],"are":[21],"often":[22],"inefficient":[23],"and":[24,45,113,132,175],"struggle":[25],"with":[26],"breaking":[27],"news.":[28],"Recent":[29],"advances":[30],"in":[31,75,171],"large":[32],"language":[33],"models":[34],"(LLMs)":[35],"enable":[36],"leveraging":[37],"externally":[38],"retrieved":[39],"reports":[40,50],"as":[41],"evidence":[42,131],"for":[43,65],"explanation":[46,64,161],"generation,":[47],"but":[48],"unverified":[49,97],"may":[51],"introduce":[52,138],"inaccuracies.":[53],"Moreover,":[54],"effective":[55],"explainable":[56],"should":[60],"provide":[61],"a":[62,69,85,102,139],"comprehensible":[63],"all":[66],"aspects":[67],"claim":[70,109],"assist":[72],"public":[74],"verifying":[76],"its":[77,179],"accuracy.":[78],"To":[79],"address":[80],"these":[81],"challenges,":[82],"we":[83,100,121,153],"propose":[84],"graph-enhanced":[86],"defense":[87],"framework":[88],"(G-Defense)":[89],"that":[90,166],"provides":[91],"fine-grained":[92],"explanations":[93],"based":[94,143],"solely":[95],"on":[96,144],"reports.":[98],"Specifically,":[99],"construct":[101],"claim-centered":[103],"graph":[104,146],"by":[105],"decomposing":[106],"into":[110],"several":[111],"sub-claims":[112],"modeling":[114],"their":[115],"dependency":[116],"relationships.":[117],"For":[118],"each":[119],"sub-claim,":[120],"use":[122],"retrieval-augmented":[124],"generation":[125],"(RAG)":[126],"technique":[127],"retrieve":[129],"salient":[130],"generate":[133,158],"competing":[134],"We":[136],"then":[137],"defense-like":[140],"inference":[141],"module":[142],"overall":[150],"veracity.":[151],"Finally,":[152],"prompt":[154],"an":[155,159],"LLM":[156],"intuitive":[160],"graph.":[162],"Experimental":[163],"results":[164],"demonstrate":[165],"G-Defense":[167],"achieves":[168],"state-of-the-art":[169],"performance":[170],"both":[172],"quality":[177]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-10T00:00:00"}
