{"id":"https://openalex.org/W3214231390","doi":"https://doi.org/10.1145/3461614","title":"Enriching Conventional Ensemble Learner with Deep Contextual Semantics to Detect Fake News in Urdu","display_name":"Enriching Conventional Ensemble Learner with Deep Contextual Semantics to Detect Fake News in Urdu","publication_year":2021,"publication_date":"2021-11-10","ids":{"openalex":"https://openalex.org/W3214231390","doi":"https://doi.org/10.1145/3461614","mag":"3214231390"},"language":"en","primary_location":{"id":"doi:10.1145/3461614","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3461614","pdf_url":null,"source":{"id":"https://openalex.org/S4306421405","display_name":"ACM Transactions on Asian and Low-Resource Language Information Processing","issn_l":"2375-4699","issn":["2375-4699","2375-4702"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Asian and Low-Resource Language Information Processing","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/A5038254380","display_name":"Ramsha Saeed","orcid":"https://orcid.org/0000-0002-9504-0368"},"institutions":[{"id":"https://openalex.org/I929597975","display_name":"National University of Sciences and Technology","ror":"https://ror.org/03w2j5y17","country_code":"PK","type":"education","lineage":["https://openalex.org/I929597975"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Ramsha Saeed","raw_affiliation_strings":["National University of Sciences and Technology (NUST), Islamabad, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Sciences and Technology (NUST), Islamabad, Pakistan","institution_ids":["https://openalex.org/I929597975"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009591817","display_name":"Hammad Afzal","orcid":"https://orcid.org/0000-0001-9583-5585"},"institutions":[{"id":"https://openalex.org/I929597975","display_name":"National University of Sciences and Technology","ror":"https://ror.org/03w2j5y17","country_code":"PK","type":"education","lineage":["https://openalex.org/I929597975"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Hammad Afzal","raw_affiliation_strings":["National University of Sciences and Technology (NUST), Islamabad, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Sciences and Technology (NUST), Islamabad, Pakistan","institution_ids":["https://openalex.org/I929597975"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023413248","display_name":"Haider Abbas","orcid":null},"institutions":[{"id":"https://openalex.org/I929597975","display_name":"National University of Sciences and Technology","ror":"https://ror.org/03w2j5y17","country_code":"PK","type":"education","lineage":["https://openalex.org/I929597975"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Haider Abbas","raw_affiliation_strings":["National University of Sciences and Technology (NUST), Islamabad, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Sciences and Technology (NUST), Islamabad, Pakistan","institution_ids":["https://openalex.org/I929597975"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071942149","display_name":"Maheen Fatima","orcid":"https://orcid.org/0009-0000-1804-0578"},"institutions":[{"id":"https://openalex.org/I929597975","display_name":"National University of Sciences and Technology","ror":"https://ror.org/03w2j5y17","country_code":"PK","type":"education","lineage":["https://openalex.org/I929597975"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Maheen Fatima","raw_affiliation_strings":["National University of Sciences and Technology (NUST), Islamabad, Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Sciences and Technology (NUST), Islamabad, Pakistan","institution_ids":["https://openalex.org/I929597975"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1441,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.85249185,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"21","issue":"1","first_page":"1","last_page":"19"},"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":0.9851999878883362,"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":0.9488000273704529,"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":0.790009617805481},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6993308067321777},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6856848001480103},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5749222636222839},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5407921671867371},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5291450619697571},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.48889175057411194},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.48205456137657166},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.42646878957748413},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3967162072658539}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.790009617805481},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6993308067321777},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6856848001480103},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5749222636222839},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5407921671867371},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5291450619697571},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.48889175057411194},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.48205456137657166},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.42646878957748413},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3967162072658539},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3461614","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3461614","pdf_url":null,"source":{"id":"https://openalex.org/S4306421405","display_name":"ACM Transactions on Asian and Low-Resource Language Information Processing","issn_l":"2375-4699","issn":["2375-4699","2375-4702"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Asian and Low-Resource Language Information Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322799","display_name":"Higher Education Commission, Pakistan","ror":"https://ror.org/038y3sz68"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1614298861","https://openalex.org/W2026539448","https://openalex.org/W2161283199","https://openalex.org/W2607906563","https://openalex.org/W2609512471","https://openalex.org/W2611921107","https://openalex.org/W2740560510","https://openalex.org/W2742144412","https://openalex.org/W2742330194","https://openalex.org/W2759820691","https://openalex.org/W2763572884","https://openalex.org/W2790166049","https://openalex.org/W2798611956","https://openalex.org/W2798787718","https://openalex.org/W2810026022","https://openalex.org/W2900584776","https://openalex.org/W2902998547","https://openalex.org/W2903981179","https://openalex.org/W2906971970","https://openalex.org/W2911710347","https://openalex.org/W2945391245","https://openalex.org/W2946297425","https://openalex.org/W2972311433","https://openalex.org/W3007083472","https://openalex.org/W3015935667","https://openalex.org/W3036840722","https://openalex.org/W3094793799","https://openalex.org/W3101890897","https://openalex.org/W3104196571","https://openalex.org/W4205918858","https://openalex.org/W4293355737","https://openalex.org/W4293926355","https://openalex.org/W4297782361","https://openalex.org/W4300952844"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W2766514146","https://openalex.org/W2885516856","https://openalex.org/W4289703016","https://openalex.org/W3094138326","https://openalex.org/W4310224730"],"abstract_inverted_index":{"Increased":[0],"connectivity":[1],"has":[2,19,69],"contributed":[3],"greatly":[4],"in":[5,22,134],"facilitating":[6],"rapid":[7],"access":[8],"to":[9,40,51,129,171],"information":[10,17],"and":[11,168,187,196,218],"reliable":[12],"communication.":[13],"However,":[14,90],"the":[15,23,58,66,75,79,87,100,108,148,158,201,215,220],"uncontrolled":[16],"dissemination":[18],"also":[20],"resulted":[21],"spread":[24,32],"of":[25,36,60,63,78,93,99,110],"fake":[26,64,83,94,114,132,223],"news.":[27],"Fake":[28],"news":[29,84,95,115,133,224],"might":[30],"be":[31],"by":[33],"a":[34,53,124,172],"group":[35],"people":[37],"or":[38,47,50],"organizations":[39],"serve":[41],"ulterior":[42],"motives":[43],"such":[44],"as":[45],"political":[46],"financial":[48],"gains":[49],"damage":[52],"country\u2019s":[54],"public":[55],"image.":[56],"Given":[57],"importance":[59,109],"timely":[61],"detection":[62,92,116,225],"news,":[65],"research":[67,112],"area":[68],"intrigued":[70],"researchers":[71],"from":[72,147,157],"all":[73],"over":[74],"world.":[76],"Most":[77],"work":[80,122],"for":[81,103,117],"detecting":[82],"focuses":[85],"on":[86,113,142],"English":[88],"language.":[89,138],"automated":[91],"is":[96,152],"important":[97],"irrespective":[98],"language":[101],"used":[102],"spreading":[104],"false":[105],"information.":[106],"Recognizing":[107],"boosting":[111],"low":[118,136],"resource":[119,137],"languages,":[120],"this":[121],"proposes":[123],"novel":[125],"semantically":[126],"enriched":[127],"technique":[128],"effectively":[130],"detect":[131],"Urdu\u2014a":[135],"A":[139],"model":[140],"based":[141],"deep":[143,206],"contextual":[144,207],"semantics":[145,208],"learned":[146,156],"convolutional":[149,159],"neural":[150,160],"network":[151,161],"proposed.":[153],"The":[154],"features":[155,167,213],"are":[162,169,191],"combined":[163],"with":[164,179,193,205,210],"other":[165,211],"n-gram-based":[166],"fed":[170],"conventional":[173],"majority":[174],"voting":[175],"ensemble":[176,203],"classifier":[177],"fitted":[178],"three":[180],"base":[181],"learners:":[182],"Adaptive":[183],"Boosting,":[184,186],"Gradient":[185],"Multi-Layer":[188],"Perceptron.":[189],"Experiments":[190],"performed":[192],"different":[194],"models,":[195],"results":[197,217],"show":[198],"that":[199],"enriching":[200],"traditional":[202],"learner":[204],"along":[209],"standard":[212],"shows":[214],"best":[216],"outperforms":[219],"state-of-the-art":[221],"Urdu":[222],"model.":[226]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}