{"id":"https://openalex.org/W7131100000","doi":"https://doi.org/10.1145/3779211.3793174","title":"WISE: Web Information Satire and Fakeness Evaluation","display_name":"WISE: Web Information Satire and Fakeness Evaluation","publication_year":2026,"publication_date":"2026-02-22","ids":{"openalex":"https://openalex.org/W7131100000","doi":"https://doi.org/10.1145/3779211.3793174"},"language":null,"primary_location":{"id":"doi:10.1145/3779211.3793174","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3779211.3793174","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3779211.3793174","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5116405783","display_name":"Gaurab Chhetri","orcid":"https://orcid.org/0009-0000-0124-4814"},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Gaurab Chhetri","raw_affiliation_strings":["Texas State University, San Marcos, USA"],"raw_orcid":"https://orcid.org/0009-0000-0124-4814","affiliations":[{"raw_affiliation_string":"Texas State University, San Marcos, USA","institution_ids":["https://openalex.org/I13511017"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120938520","display_name":"Subasish Das","orcid":null},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Subasish Das","raw_affiliation_strings":["Texas State University, San Marcos, USA"],"raw_orcid":"https://orcid.org/0000-0002-1671-2753","affiliations":[{"raw_affiliation_string":"Texas State University, San Marcos, USA","institution_ids":["https://openalex.org/I13511017"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126625593","display_name":"Tausif Islam Chowdhury","orcid":null},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tausif Islam Chowdhury","raw_affiliation_strings":["Texas State University, San Marcos, USA"],"raw_orcid":"https://orcid.org/0009-0008-2385-8719","affiliations":[{"raw_affiliation_string":"Texas State University, San Marcos, USA","institution_ids":["https://openalex.org/I13511017"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5116405783"],"corresponding_institution_ids":["https://openalex.org/I13511017"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28413509,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"93","last_page":"102"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":0.9799000024795532,"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":0.9799000024795532,"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":0.005900000222027302,"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/T14347","display_name":"Big Data and Digital Economy","score":0.001500000013038516,"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/mcnemars-test","display_name":"McNemar's test","score":0.6226999759674072},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.5835999846458435},{"id":"https://openalex.org/keywords/misinformation","display_name":"Misinformation","score":0.5246000289916992},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.3504999876022339},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.28940001130104065},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.2840000092983246}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6883999705314636},{"id":"https://openalex.org/C186282968","wikidata":"https://www.wikidata.org/wiki/Q1434261","display_name":"McNemar's test","level":2,"score":0.6226999759674072},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5835999846458435},{"id":"https://openalex.org/C2776990098","wikidata":"https://www.wikidata.org/wiki/Q13579947","display_name":"Misinformation","level":2,"score":0.5246000289916992},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.45320001244544983},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3772999942302704},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.3504999876022339},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33559998869895935},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3345000147819519},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.32010000944137573},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.2840000092983246},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28380000591278076},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2825999855995178},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.25920000672340393},{"id":"https://openalex.org/C44083865","wikidata":"https://www.wikidata.org/wiki/Q3853443","display_name":"Mean reciprocal rank","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3779211.3793174","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3779211.3793174","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2512.24000","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.24000","pdf_url":"https://arxiv.org/pdf/2512.24000","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3779211.3793174","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3779211.3793174","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.5271585583686829,"display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Distinguishing":[0],"fake":[1,55],"or":[2,7,57,147],"untrue":[3],"news":[4,56],"from":[5,50],"satire":[6],"humor":[8],"poses":[9],"a":[10,44,88],"unique":[11],"challenge":[12],"due":[13],"to":[14],"their":[15],"overlapping":[16],"linguistic":[17],"features":[18],"and":[19,29,79,106,119,133],"divergent":[20],"intent.":[21],"This":[22],"study":[23],"develops":[24],"WISE":[25],"(Web":[26],"Information":[27],"Satire":[28],"Fakeness":[30],"Evaluation)":[31],"framework,":[32],"which":[33],"benchmarks":[34],"eight":[35],"lightweight":[36,89,143],"transformer":[37],"models":[38,42,65,144],"alongside":[39],"two":[40],"baseline":[41,149],"on":[43],"balanced":[45],"dataset":[46],"of":[47],"20,000":[48],"samples":[49],"Fakeddit,":[51],"annotated":[52],"as":[53],"either":[54],"satire.":[58],"Using":[59],"stratified":[60],"5-fold":[61],"cross-validation,":[62],"we":[63],"evaluate":[64],"across":[66],"comprehensive":[67],"metrics":[68],"including":[69],"accuracy,":[70],"precision,":[71],"recall,":[72],"F1-score,":[73],"ROC-AUC,":[74],"PR-AUC,":[75],"MCC,":[76],"Brier":[77],"score,":[78],"Expected":[80],"Calibration":[81],"Error.":[82],"Our":[83,139],"evaluation":[84],"reveals":[85],"that":[86,142],"MiniLM,":[87],"model,":[90],"achieves":[91,101],"the":[92,102],"highest":[93,103],"accuracy":[94,108,118],"(87.58%)":[95],"among":[96],"all":[97],"models,":[98,129],"while":[99],"RoBERTa-base":[100],"ROC-AUC":[104],"(95.42%)":[105],"strong":[107],"(87.36%).":[109],"DistilBERT":[110],"offers":[111],"an":[112],"excellent":[113],"efficiency-accuracy":[114],"trade-off":[115],"with":[116,130],"86.28%":[117],"93.90%":[120],"ROC-AUC.":[121],"Statistical":[122],"tests":[123,135],"confirm":[124],"significant":[125],"performance":[126],"differences":[127],"between":[128],"paired":[131],"t-tests":[132],"McNemar":[134],"providing":[136],"rigorous":[137],"comparisons.":[138],"findings":[140],"highlight":[141],"can":[145],"match":[146],"exceed":[148],"performance,":[150],"offering":[151],"actionable":[152],"insights":[153],"for":[154],"deploying":[155],"misinformation":[156],"detection":[157],"systems":[158],"in":[159],"real-world,":[160],"resource-constrained":[161],"settings.":[162]},"counts_by_year":[],"updated_date":"2026-05-06T06:10:43.113611","created_date":"2026-02-24T00:00:00"}
