{"id":"https://openalex.org/W4200425753","doi":"https://doi.org/10.1109/gcce53005.2021.9621803","title":"Propagation-Based Fake News Detection Using Graph Neural Networks with Transformer","display_name":"Propagation-Based Fake News Detection Using Graph Neural Networks with Transformer","publication_year":2021,"publication_date":"2021-10-12","ids":{"openalex":"https://openalex.org/W4200425753","doi":"https://doi.org/10.1109/gcce53005.2021.9621803"},"language":"en","primary_location":{"id":"doi:10.1109/gcce53005.2021.9621803","doi":"https://doi.org/10.1109/gcce53005.2021.9621803","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcce53005.2021.9621803","pdf_url":null,"source":{"id":"https://openalex.org/S4363607800","display_name":"IEEE Global Conference on Consumer Electronics (GCCE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2021 IEEE 10th Global Conference on Consumer Electronics (GCCE)","raw_type":"proceedings-article"},"volume":null,"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":null,"display_name":"Hayato Matsumoto","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I56624758","display_name":"Kansai University","ror":"https://ror.org/03xg1f311","country_code":"JP","type":"education","lineage":["https://openalex.org/I56624758"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hayato Matsumoto","raw_affiliation_strings":["Graduate School of Science and Engineering, Kansai University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Engineering, Kansai University","institution_ids":["https://openalex.org/I56624758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061967814","display_name":"Soh Yoshida","orcid":"https://orcid.org/0000-0003-0237-7461","observed_orcids":["https://orcid.org/0000-0003-0237-7461"]},"institutions":[{"id":"https://openalex.org/I56624758","display_name":"Kansai University","ror":"https://ror.org/03xg1f311","country_code":"JP","type":"education","lineage":["https://openalex.org/I56624758"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Soh Yoshida","raw_affiliation_strings":["Graduate School of Science and Engineering, Kansai University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Engineering, Kansai University","institution_ids":["https://openalex.org/I56624758"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089184180","display_name":"Mitsuji Muneyasu","orcid":"https://orcid.org/0000-0002-4492-5991","observed_orcids":["https://orcid.org/0000-0002-4492-5991"]},"institutions":[{"id":"https://openalex.org/I56624758","display_name":"Kansai University","ror":"https://ror.org/03xg1f311","country_code":"JP","type":"education","lineage":["https://openalex.org/I56624758"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Mitsuji Muneyasu","raw_affiliation_strings":["Graduate School of Science and Engineering, Kansai University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Engineering, Kansai University","institution_ids":["https://openalex.org/I56624758"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I56624758"],"apc_list":null,"apc_paid":null,"fwci":13.3302,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.98736819,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"19","last_page":"20"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":0.9994999766349792,"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":0.9994999766349792,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":0.0003000000142492354,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/fake-news-detection","display_name":"fake news detection","score":1.0},{"id":"https://openalex.org/keywords/graph-neural-networks","display_name":"graph neural networks","score":0.8550000190734863},{"id":"https://openalex.org/keywords/graph-transformer-networks","display_name":"graph transformer networks","score":0.9190000295639038},{"id":"https://openalex.org/keywords/social-media-misinformation","display_name":"social media misinformation","score":0.7250000238418579},{"id":"https://openalex.org/keywords/user-preference-learning","display_name":"user preference learning","score":0.6919999718666077},{"id":"https://openalex.org/keywords/twitter","display_name":"Twitter","score":0.6269999742507935}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7035355567932129},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5170215368270874},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5091633200645447},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4175880551338196},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39912667870521545},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.24944564700126648},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.15166142582893372},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11908870935440063},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.09005725383758545}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/gcce53005.2021.9621803","doi":"https://doi.org/10.1109/gcce53005.2021.9621803","is_oa":false,"landing_page_url":"https://doi.org/10.1109/gcce53005.2021.9621803","pdf_url":null,"source":{"id":"https://openalex.org/S4363607800","display_name":"IEEE Global Conference on Consumer Electronics (GCCE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2021 IEEE 10th Global Conference on Consumer Electronics (GCCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320338246","display_name":"ACT-X","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2896457183","https://openalex.org/W2907492528","https://openalex.org/W2911710347","https://openalex.org/W2963341956","https://openalex.org/W3023903980","https://openalex.org/W3031781733","https://openalex.org/W3153567752","https://openalex.org/W3187966659","https://openalex.org/W4288079542"],"related_works":["https://openalex.org/W7140283986","https://openalex.org/W4221093564","https://openalex.org/W4401863559","https://openalex.org/W4401996603","https://openalex.org/W4385226847","https://openalex.org/W4361732758","https://openalex.org/W4317418522","https://openalex.org/W4312653266","https://openalex.org/W4388726702","https://openalex.org/W4385894011"],"abstract_inverted_index":{"The":[0,113],"spread":[1,23],"of":[2,115,129],"fake":[3,18,89],"news":[4,19,22,40,90],"has":[5],"become":[6],"a":[7,72,86],"worldwide":[8],"problem,":[9],"affecting":[10],"public":[11],"trust.":[12],"Recent":[13],"studies":[14],"have":[15,58],"reported":[16],"that":[17],"and":[20,39,45,50],"real":[21],"differently":[24],"on":[25],"social":[26],"media.":[27],"Thus,":[28],"propagation-based":[29],"detection":[30,91],"methods,":[31],"which":[32,97],"construct":[33],"graphs":[34],"with":[35],"users":[36],"as":[37,43],"nodes":[38,108],"sharing":[41],"chains":[42],"edges":[44],"simultaneously":[46],"learn":[47,75,99],"propagation":[48],"patterns":[49],"users\u2019":[51,65],"preferences":[52,66],"using":[53,92,124],"Graph":[54,93],"Neural":[55],"Networks":[56],"(GNNs),":[57],"attracted":[59],"much":[60],"attention.":[61],"However,":[62],"for":[63,88],"extracting":[64],"from":[67],"the":[68,76,110,116,125],"graph,":[69],"it":[70],"is":[71,119],"challenge":[73],"to":[74],"relationship":[77],"between":[78,107],"unconnected":[79],"nodes.":[80],"In":[81],"this":[82],"paper,":[83],"we":[84],"propose":[85],"method":[87,118],"Transformer":[94],"Network":[95],"(GTN),":[96],"can":[98],"efficient":[100],"node":[101],"representations":[102],"while":[103],"identifying":[104],"useful":[105],"connections":[106],"in":[109],"original":[111],"graph.":[112],"effectiveness":[114],"proposed":[117],"confirmed":[120],"by":[121],"comparison":[122],"experiments":[123],"real-world":[126],"dataset":[127],"composed":[128],"Twitter":[130],"data.":[131]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":4}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2021-12-31T00:00:00"}
