{"id":"https://openalex.org/W3217187294","doi":"https://doi.org/10.1108/dta-07-2021-0196","title":"A novel semi-supervised self-training method based on resampling for Twitter fake account identification","display_name":"A novel semi-supervised self-training method based on resampling for Twitter fake account identification","publication_year":2021,"publication_date":"2021-11-26","ids":{"openalex":"https://openalex.org/W3217187294","doi":"https://doi.org/10.1108/dta-07-2021-0196","mag":"3217187294"},"language":"en","primary_location":{"id":"doi:10.1108/dta-07-2021-0196","is_oa":false,"landing_page_url":"https://doi.org/10.1108/dta-07-2021-0196","pdf_url":null,"source":{"id":"https://openalex.org/S4210171756","display_name":"Data Technologies and Applications","issn_l":"2514-9288","issn":["2514-9288","2514-9318"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Technologies and Applications","raw_type":"journal-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/A5007941027","display_name":"Ziming Zeng","orcid":"https://orcid.org/0000-0001-9847-0358"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziming Zeng","raw_affiliation_strings":["School of Information Management, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-9847-0358","affiliations":[{"raw_affiliation_string":"School of Information Management, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082363548","display_name":"Tingting Li","orcid":"https://orcid.org/0000-0002-5651-5260"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tingting Li","raw_affiliation_strings":["School of Information Management, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-5651-5260","affiliations":[{"raw_affiliation_string":"School of Information Management, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087822689","display_name":"Shouqiang Sun","orcid":"https://orcid.org/0000-0001-8760-9254"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shouqiang Sun","raw_affiliation_strings":["School of Information Management, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-8760-9254","affiliations":[{"raw_affiliation_string":"School of Information Management, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023686653","display_name":"Jingjing Sun","orcid":"https://orcid.org/0000-0002-7555-8186"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingjing Sun","raw_affiliation_strings":["School of Information Management, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-7555-8186","affiliations":[{"raw_affiliation_string":"School of Information Management, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005347029","display_name":"Jie Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Yin","raw_affiliation_strings":["School of Information Management, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-7130-2004","affiliations":[{"raw_affiliation_string":"School of Information Management, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I37461747"],"apc_list":null,"apc_paid":null,"fwci":2.8453,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.92208392,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"56","issue":"3","first_page":"409","last_page":"428"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":0.9998999834060669,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11147","display_name":"Misinformation and Its Impacts","score":0.9965000152587891,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8061193227767944},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.6977366209030151},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6535236835479736},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5555484890937805},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5479863882064819},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5388870239257812},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.46741366386413574},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.45496493577957153},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.4404424726963043},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.4311479330062866},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.41135239601135254},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3756559193134308},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.17456650733947754}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8061193227767944},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.6977366209030151},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6535236835479736},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5555484890937805},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5479863882064819},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5388870239257812},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.46741366386413574},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.45496493577957153},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.4404424726963043},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4311479330062866},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.41135239601135254},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3756559193134308},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.17456650733947754},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1108/dta-07-2021-0196","is_oa":false,"landing_page_url":"https://doi.org/10.1108/dta-07-2021-0196","pdf_url":null,"source":{"id":"https://openalex.org/S4210171756","display_name":"Data Technologies and Applications","issn_l":"2514-9288","issn":["2514-9288","2514-9318"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Technologies and Applications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.550000011920929,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W1837843568","https://openalex.org/W1966748751","https://openalex.org/W2014802425","https://openalex.org/W2029677459","https://openalex.org/W2039954341","https://openalex.org/W2080913999","https://openalex.org/W2109939070","https://openalex.org/W2210596465","https://openalex.org/W2278635123","https://openalex.org/W2350142359","https://openalex.org/W2408246687","https://openalex.org/W2460286025","https://openalex.org/W2512345558","https://openalex.org/W2547680457","https://openalex.org/W2560070550","https://openalex.org/W2579367737","https://openalex.org/W2583516892","https://openalex.org/W2595521492","https://openalex.org/W2618516756","https://openalex.org/W2624661262","https://openalex.org/W2711122919","https://openalex.org/W2743980629","https://openalex.org/W2767054107","https://openalex.org/W2786537343","https://openalex.org/W2787296320","https://openalex.org/W2797928606","https://openalex.org/W2800675387","https://openalex.org/W2800788706","https://openalex.org/W2801279631","https://openalex.org/W2801331634","https://openalex.org/W2897138847","https://openalex.org/W2913773671","https://openalex.org/W2936503027","https://openalex.org/W2937643694","https://openalex.org/W2943865428","https://openalex.org/W2946856970","https://openalex.org/W2954996726","https://openalex.org/W2962783931","https://openalex.org/W2990363553","https://openalex.org/W3015177213","https://openalex.org/W3017247345","https://openalex.org/W3018236601","https://openalex.org/W3040200962","https://openalex.org/W3098147746","https://openalex.org/W3099802519","https://openalex.org/W3104378754","https://openalex.org/W3125182500","https://openalex.org/W4231258178"],"related_works":["https://openalex.org/W4292388283","https://openalex.org/W3204418343","https://openalex.org/W1560624709","https://openalex.org/W3166286441","https://openalex.org/W3214142563","https://openalex.org/W3111760155","https://openalex.org/W3170999895","https://openalex.org/W4389374014","https://openalex.org/W34092691","https://openalex.org/W4312414840"],"abstract_inverted_index":{"Purpose":[0],"Twitter":[1,52,96,147,212,222,239],"fake":[2,41,223],"accounts":[3,7,26,240],"refer":[4],"to":[5,12,29,49,72,93,123,139,236,255],"bot":[6,25],"created":[8],"by":[9],"third-party":[10],"organizations":[11],"influence":[13,192,259],"public":[14],"opinion,":[15],"commercial":[16],"propaganda":[17],"or":[18],"impersonate":[19],"others.":[20],"The":[21,182],"effective":[22],"identification":[23,199,265],"of":[24,86,114,177,193,260],"is":[27,45,151,154,161,171,249],"conducive":[28],"accurately":[30],"judge":[31],"the":[32,36,55,66,78,81,84,94,103,107,110,120,141,158,164,168,174,178,191,208,226,229,242,246,252,258,264],"disseminated":[33],"information":[34],"for":[35,207],"public.":[37],"However,":[38],"in":[39,61,109,220,231],"actual":[40],"account":[42,97,116,128,148],"identification,":[43],"it":[44,92],"expensive":[46],"and":[47,54,90,167,189],"inefficient":[48],"manually":[50],"label":[51,125,238],"accounts,":[53],"labeled":[56,115,142,211],"data":[57,98,138,169],"are":[58],"usually":[59],"unbalanced":[60],"classes.":[62],"To":[63],"this":[64],"end,":[65],"authors":[67,82,104,227],"propose":[68],"a":[69,233],"novel":[70,218],"framework":[71,184],"solve":[73],"these":[74],"problems.":[75],"Design/methodology/approach":[76],"In":[77],"proposed":[79,183],"framework,":[80],"introduce":[83],"concept":[85],"semi-supervised":[87,243],"self-training":[88,165,179,234,253],"learning":[89],"apply":[91],"real":[95],"set":[99,150],"from":[100,136,241],"Kaggle.":[101],"Specifically,":[102],"first":[105],"train":[106],"classifier":[108,122],"initial":[111,175,209],"small":[112],"amount":[113],"data,":[117],"then":[118],"use":[119],"trained":[121],"automatically":[124,237],"large-scale":[126],"unlabeled":[127,137],"data.":[129,143],"Next,":[130],"iteratively":[131],"select":[132],"high":[133],"confidence":[134],"instances":[135],"expand":[140],"Finally,":[144],"an":[145],"expanded":[146],"training":[149],"obtained.":[152],"It":[153,196],"worth":[155],"mentioning":[156],"that":[157],"resampling":[159,247],"technique":[160,248],"integrated":[162,250],"into":[163,251],"process,":[166],"class":[170,194,261],"balanced":[172],"at":[173],"stage":[176],"iteration.":[180],"Findings":[181],"effectively":[185,256],"improves":[186],"labeling":[187],"efficiency":[188],"reduces":[190],"imbalance.":[195],"shows":[197],"excellent":[198],"results":[200],"on":[201,263],"6":[202],"different":[203],"base":[204],"classifiers,":[205],"especially":[206],"small-scale":[210],"accounts.":[213,224],"Originality/value":[214],"This":[215],"paper":[216],"provides":[217],"insights":[219],"identifying":[221],"First,":[225],"take":[228],"lead":[230],"introducing":[232],"method":[235],"background.":[244],"Second,":[245],"process":[254],"reduce":[257],"imbalance":[262],"effect.":[266]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}