{"id":"https://openalex.org/W2546513253","doi":"https://doi.org/10.1145/2998476.2998498","title":"A Novel Approach to Big Data Veracity using Crowdsourcing Techniques and Bayesian Predictors","display_name":"A Novel Approach to Big Data Veracity using Crowdsourcing Techniques and Bayesian Predictors","publication_year":2016,"publication_date":"2016-10-21","ids":{"openalex":"https://openalex.org/W2546513253","doi":"https://doi.org/10.1145/2998476.2998498","mag":"2546513253"},"language":"en","primary_location":{"id":"doi:10.1145/2998476.2998498","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2998476.2998498","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th Annual ACM India Conference","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/A5089847231","display_name":"Bhoomika Agarwal","orcid":null},"institutions":[{"id":"https://openalex.org/I196608512","display_name":"PES University","ror":"https://ror.org/05m169e78","country_code":"IN","type":"education","lineage":["https://openalex.org/I196608512"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Bhoomika Agarwal","raw_affiliation_strings":["PES Institute of Technology, Bangalore South Campus"],"affiliations":[{"raw_affiliation_string":"PES Institute of Technology, Bangalore South Campus","institution_ids":["https://openalex.org/I196608512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011860168","display_name":"Abhiram Ravikumar","orcid":"https://orcid.org/0000-0003-0418-1278"},"institutions":[{"id":"https://openalex.org/I196608512","display_name":"PES University","ror":"https://ror.org/05m169e78","country_code":"IN","type":"education","lineage":["https://openalex.org/I196608512"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Abhiram Ravikumar","raw_affiliation_strings":["PES Institute of Technology, Bangalore South Campus"],"affiliations":[{"raw_affiliation_string":"PES Institute of Technology, Bangalore South Campus","institution_ids":["https://openalex.org/I196608512"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066292961","display_name":"Snehanshu Saha","orcid":"https://orcid.org/0000-0002-8458-604X"},"institutions":[{"id":"https://openalex.org/I196608512","display_name":"PES University","ror":"https://ror.org/05m169e78","country_code":"IN","type":"education","lineage":["https://openalex.org/I196608512"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Snehanshu Saha","raw_affiliation_strings":["PES Institute of Technology, Bangalore South Campus"],"affiliations":[{"raw_affiliation_string":"PES Institute of Technology, Bangalore South Campus","institution_ids":["https://openalex.org/I196608512"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5089847231"],"corresponding_institution_ids":["https://openalex.org/I196608512"],"apc_list":null,"apc_paid":null,"fwci":0.8569,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.84355038,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"153","last_page":"160"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9955000281333923,"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"}},"topics":[{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9955000281333923,"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/T11644","display_name":"Spam and Phishing Detection","score":0.9940999746322632,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9940000176429749,"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/crowdsourcing","display_name":"Crowdsourcing","score":0.8761992454528809},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.7813123464584351},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7295746803283691},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.704718291759491},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5911586284637451},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.5301448106765747},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.49315541982650757},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4927847683429718},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41222119331359863},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3537721633911133},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3394039273262024},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3355885446071625},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.08773821592330933},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.07327800989151001}],"concepts":[{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.8761992454528809},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.7813123464584351},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7295746803283691},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.704718291759491},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5911586284637451},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.5301448106765747},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.49315541982650757},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4927847683429718},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41222119331359863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3537721633911133},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3394039273262024},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3355885446071625},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.08773821592330933},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.07327800989151001},{"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:10.1145/2998476.2998498","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2998476.2998498","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th Annual ACM India Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.7900000214576721,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1560791848","https://openalex.org/W2000877723","https://openalex.org/W2072479218","https://openalex.org/W2078574033","https://openalex.org/W2116947789","https://openalex.org/W2166706824","https://openalex.org/W2198331448","https://openalex.org/W2295853830"],"related_works":["https://openalex.org/W3032998312","https://openalex.org/W135177976","https://openalex.org/W4384486036","https://openalex.org/W1503094549","https://openalex.org/W2337920774","https://openalex.org/W4286908577","https://openalex.org/W2886410948","https://openalex.org/W2025875869","https://openalex.org/W4318823662","https://openalex.org/W3207526114"],"abstract_inverted_index":{"In":[0,82],"today's":[1],"world":[2],"data":[3,31,46,56,60,80,92,177,193,261],"is":[4,52,252],"being":[5],"generated":[6],"at":[7],"a":[8,63,114,119,175,185,195,200,226,253],"tremendous":[9],"pace":[10],"and":[11,37,74,153,188,219,263],"there":[12],"have":[13,124,245],"to":[14,20,34,77,89,131,157,174,256],"be":[15,205],"enough":[16],"measures":[17],"in":[18,66,113,140,267],"place":[19],"verify":[21],"the":[22,59,79,90,101,110,136,154,158,169,216,222,230,237,257],"nature":[23],"of":[24,44,103,116,121,150,209,229,249,259],"big":[25,45,55,91,260],"data.":[26],"Analysis":[27],"performed":[28],"on":[29,184],"'dirty'":[30],"may":[32],"lead":[33],"erroneous":[35],"insights":[36],"thereby":[38],"shaping":[39],"decisions":[40,68],"poorly.":[41],"The":[42,162],"aspect":[43],"that":[47,128,247],"deals":[48,107],"with":[49,108,199],"its":[50],"correctness":[51],"known":[53],"as":[54,134,172,213,236],"veracity.":[57],"Trusting":[58],"acquired":[61],"goes":[62],"long":[64],"way":[65],"implementing":[67],"from":[69],"an":[70,126,207,265],"automated":[71],"decision-making":[72],"system":[73],"veracity":[75,93,262],"helps":[76],"validate":[78],"acquired.":[81],"this":[83],"paper,":[84],"we":[85,123,244],"present":[86],"our":[87,151],"solution":[88,99,255],"problem":[94,258],"using":[95,194],"crowdsourcing":[96,248],"techniques.":[97],"Our":[98],"involves":[100],"use":[102],"sentiment":[104,111,137,155,235,250],"analysis,":[105],"which":[106],"identifying":[109],"expressed":[112],"piece":[115],"text.":[117],"As":[118,203],"proof":[120],"concept,":[122],"developed":[125],"app":[127],"requires":[129],"users":[130],"tag":[132],"tweets":[133],"per":[135],"it":[138],"evokes":[139],"them.":[141],"Each":[142],"tweet":[143,159],"would":[144],"therefore":[145,264],"get":[146],"ratified":[147],"by":[148,215],"hundreds":[149],"participants":[152],"associated":[156],"gets":[160],"tagged.":[161],"tagged":[163],"emotion":[164,171],"was":[165,181,211],"then":[166,182],"evaluated":[167,190],"against":[168,191],"verified":[170,176,192],"compared":[173],"set.":[178],"This":[179],"analysis":[180,228,251],"plotted":[183],"ROC":[186,217],"curve":[187,218],"also":[189],"Bayesian":[196,223,231],"predictor":[197,232],"trained":[198],"trinomial":[201],"function.":[202],"can":[204],"seen,":[206],"accuracy":[208],"81%":[210],"obtained":[212],"displayed":[214],"89%":[220],"through":[221],"predictor.":[224],"Also,":[225],"MAP":[227],"yields":[233],"neutral":[234],"most":[238],"probable":[239],"hypothesis.":[240],"By":[241],"doing":[242],"this,":[243],"proven":[246],"viable":[254],"aid":[266],"making":[268],"better":[269],"decisions.":[270]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
