{"id":"https://openalex.org/W2977130232","doi":"https://doi.org/10.1145/3341981.3344229","title":"SADHAN","display_name":"SADHAN","publication_year":2019,"publication_date":"2019-09-26","ids":{"openalex":"https://openalex.org/W2977130232","doi":"https://doi.org/10.1145/3341981.3344229","mag":"2977130232"},"language":"en","primary_location":{"id":"doi:10.1145/3341981.3344229","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341981.3344229","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval","raw_type":"proceedings-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/A5028623584","display_name":"Rahul Mishra","orcid":"https://orcid.org/0000-0002-0976-6737"},"institutions":[{"id":"https://openalex.org/I92008406","display_name":"University of Stavanger","ror":"https://ror.org/02qte9q33","country_code":"NO","type":"education","lineage":["https://openalex.org/I92008406"]}],"countries":["NO"],"is_corresponding":true,"raw_author_name":"Rahul Mishra","raw_affiliation_strings":["University of Stavanger, Stavanger, Norway"],"affiliations":[{"raw_affiliation_string":"University of Stavanger, Stavanger, Norway","institution_ids":["https://openalex.org/I92008406"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044722424","display_name":"Vinay Setty","orcid":"https://orcid.org/0000-0002-9777-6758"},"institutions":[{"id":"https://openalex.org/I92008406","display_name":"University of Stavanger","ror":"https://ror.org/02qte9q33","country_code":"NO","type":"education","lineage":["https://openalex.org/I92008406"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Vinay Setty","raw_affiliation_strings":["University of Stavanger, Stavanger, Norway"],"affiliations":[{"raw_affiliation_string":"University of Stavanger, Stavanger, Norway","institution_ids":["https://openalex.org/I92008406"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5028623584"],"corresponding_institution_ids":["https://openalex.org/I92008406"],"apc_list":null,"apc_paid":null,"fwci":6.3882,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.96271747,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"197","last_page":"204"},"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/T10028","display_name":"Topic Modeling","score":0.9987000226974487,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9950000047683716,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8303419351577759},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.679414689540863},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.6285431981086731},{"id":"https://openalex.org/keywords/misinformation","display_name":"Misinformation","score":0.6248398423194885},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5487754940986633},{"id":"https://openalex.org/keywords/lexicon","display_name":"Lexicon","score":0.5356993079185486},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5127435922622681},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49811768531799316},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.4873405396938324},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4867272675037384},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.42916208505630493},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.4264846444129944},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.42158204317092896},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3616839647293091},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.19373482465744019},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.11795875430107117}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8303419351577759},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.679414689540863},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.6285431981086731},{"id":"https://openalex.org/C2776990098","wikidata":"https://www.wikidata.org/wiki/Q13579947","display_name":"Misinformation","level":2,"score":0.6248398423194885},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5487754940986633},{"id":"https://openalex.org/C2778121359","wikidata":"https://www.wikidata.org/wiki/Q8096","display_name":"Lexicon","level":2,"score":0.5356993079185486},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5127435922622681},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49811768531799316},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.4873405396938324},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4867272675037384},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.42916208505630493},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.4264846444129944},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.42158204317092896},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3616839647293091},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.19373482465744019},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.11795875430107117},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3341981.3344229","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3341981.3344229","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.6600000262260437}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1566256432","https://openalex.org/W1832693441","https://openalex.org/W2413794162","https://openalex.org/W2470673105","https://openalex.org/W2593408211","https://openalex.org/W2594382603","https://openalex.org/W2735017898","https://openalex.org/W2759820691","https://openalex.org/W2763572884","https://openalex.org/W2773007494","https://openalex.org/W2783564496","https://openalex.org/W2789566302","https://openalex.org/W2798787718","https://openalex.org/W2808228212","https://openalex.org/W2886390638","https://openalex.org/W2890801081","https://openalex.org/W2906971970","https://openalex.org/W2912078280","https://openalex.org/W2963523292","https://openalex.org/W3000155280","https://openalex.org/W3101890897","https://openalex.org/W4297623738"],"related_works":["https://openalex.org/W2726375170","https://openalex.org/W2785740378","https://openalex.org/W4390421161","https://openalex.org/W2912503608","https://openalex.org/W3119513105","https://openalex.org/W2590462354","https://openalex.org/W2473593971","https://openalex.org/W3002034200","https://openalex.org/W3081652108","https://openalex.org/W3097901707"],"abstract_inverted_index":{"Recently":[0],"false":[1,110,140,156],"claims":[2,111,194],"and":[3,15,24,38,59,127,181],"misinformation":[4],"have":[5],"become":[6],"rampant":[7],"in":[8,163],"the":[9,104,153,183,186,193],"web,":[10],"affecting":[11],"election":[12],"outcomes,":[13],"societies":[14],"economies.":[16],"Consequently,":[17],"fact":[18],"checking":[19],"websites":[20,31],"such":[21,123],"as":[22,124],"snopes.com":[23],"politifact.com":[25],"are":[26,136],"becoming":[27],"popular.":[28],"However,":[29],"these":[30,47,132],"require":[32],"expert":[33],"analysis":[34],"which":[35,95,189],"is":[36],"slow":[37],"not":[39],"scalable.":[40],"Many":[41],"recent":[42],"works":[43],"try":[44],"to":[45,69,82,92,161,169],"solve":[46],"challenges":[48],"using":[49],"machine":[50],"learning":[51],"models":[52],"trained":[53],"on":[54],"a":[55,60,170],"variety":[56],"of":[57,89,139,155,165],"features":[58],"rich":[61],"lexicon":[62],"or":[63,191],"more":[64],"recently,":[65],"deep":[66,79],"neural":[67],"networks":[68,81],"avoid":[70],"feature":[71],"engineering.":[72],"In":[73],"this":[74],"paper,":[75],"we":[76,129],"propose":[77],"hierarchical":[78,114],"attention":[80,150,172],"learn":[83],"embeddings":[84,107,135,147],"for":[85,108],"various":[86],"latent":[87,105,145],"aspects":[88],"news.":[90],"Contrary":[91],"existing":[93],"solutions":[94],"only":[96],"apply":[97],"word-level":[98],"self-attention,":[99],"our":[100],"model":[101],"jointly":[102],"learns":[103],"aspect":[106,134,146],"classifying":[109],"by":[112,159,175],"applying":[113],"attention.":[115],"Using":[116],"several":[117],"manually":[118],"annotated":[119],"high":[120],"quality":[121],"datasets":[122],"Politifact,":[125],"Snopes":[126],"Fever":[128],"show":[130,143],"that":[131,144],"learned":[133,148],"strong":[137],"predictors":[138],"claims.":[141],"We":[142,178],"from":[149,185],"mechanisms":[151],"improve":[152],"accuracy":[154],"claim":[157],"detection":[158],"up":[160],"13.5%":[162],"terms":[164],"Macro":[166],"F1":[167],"compared":[168],"state-of-the-art":[171],"mechanism":[173],"guided":[174],"claim-text":[176],"DeClarE.":[177],"also":[179],"extract":[180],"visualize":[182],"evidence":[184],"external":[187],"articles":[188],"supports":[190],"disproves":[192]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":4}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2019-10-03T00:00:00"}
