{"id":"https://openalex.org/W2978405523","doi":"https://doi.org/10.1109/ijcnn.2019.8852080","title":"Social Network Polluting Contents Detection through Deep Learning Techniques","display_name":"Social Network Polluting Contents Detection through Deep Learning Techniques","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2978405523","doi":"https://doi.org/10.1109/ijcnn.2019.8852080","mag":"2978405523"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852080","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852080","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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/A5101546784","display_name":"Fabio Martinelli","orcid":"https://orcid.org/0000-0002-6721-9395"},"institutions":[{"id":"https://openalex.org/I4210130157","display_name":"Institute of Informatics and Telematics","ror":"https://ror.org/02gdcn153","country_code":"IT","type":"facility","lineage":["https://openalex.org/I4210130157","https://openalex.org/I4210155236"]},{"id":"https://openalex.org/I4210155236","display_name":"National Research Council","ror":"https://ror.org/04zaypm56","country_code":"IT","type":"funder","lineage":["https://openalex.org/I4210155236"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Fabio Martinelli","raw_affiliation_strings":["Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy"],"affiliations":[{"raw_affiliation_string":"Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy","institution_ids":["https://openalex.org/I4210130157","https://openalex.org/I4210155236"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023695406","display_name":"Francesco Mercaldo","orcid":"https://orcid.org/0000-0002-9425-1657"},"institutions":[{"id":"https://openalex.org/I129627893","display_name":"University of Molise","ror":"https://ror.org/04z08z627","country_code":"IT","type":"education","lineage":["https://openalex.org/I129627893"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Francesco Mercaldo","raw_affiliation_strings":["Department of Bioscience and Territory, University of Molise, Pesche (IS), Italy"],"affiliations":[{"raw_affiliation_string":"Department of Bioscience and Territory, University of Molise, Pesche (IS), Italy","institution_ids":["https://openalex.org/I129627893"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004599844","display_name":"Antonella Santone","orcid":"https://orcid.org/0000-0002-2634-4456"},"institutions":[{"id":"https://openalex.org/I4210130157","display_name":"Institute of Informatics and Telematics","ror":"https://ror.org/02gdcn153","country_code":"IT","type":"facility","lineage":["https://openalex.org/I4210130157","https://openalex.org/I4210155236"]},{"id":"https://openalex.org/I4210155236","display_name":"National Research Council","ror":"https://ror.org/04zaypm56","country_code":"IT","type":"funder","lineage":["https://openalex.org/I4210155236"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Antonella Santone","raw_affiliation_strings":["Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy"],"affiliations":[{"raw_affiliation_string":"Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy","institution_ids":["https://openalex.org/I4210130157","https://openalex.org/I4210155236"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101546784"],"corresponding_institution_ids":["https://openalex.org/I4210130157","https://openalex.org/I4210155236"],"apc_list":null,"apc_paid":null,"fwci":1.6957,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.88502353,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"7","issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9987000226974487,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6844117045402527},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5026125907897949},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3687703609466553}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6844117045402527},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5026125907897949},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3687703609466553}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8852080","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852080","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":74,"referenced_works":["https://openalex.org/W9223698","https://openalex.org/W176212337","https://openalex.org/W202178741","https://openalex.org/W1614298861","https://openalex.org/W1837843568","https://openalex.org/W1851821411","https://openalex.org/W1915012771","https://openalex.org/W1969733464","https://openalex.org/W1986678144","https://openalex.org/W1996802155","https://openalex.org/W2005556331","https://openalex.org/W2053757944","https://openalex.org/W2055981215","https://openalex.org/W2074835059","https://openalex.org/W2076063813","https://openalex.org/W2078427117","https://openalex.org/W2092300972","https://openalex.org/W2098395374","https://openalex.org/W2107202957","https://openalex.org/W2119678860","https://openalex.org/W2120111750","https://openalex.org/W2125031621","https://openalex.org/W2125347318","https://openalex.org/W2126497299","https://openalex.org/W2133893416","https://openalex.org/W2150341604","https://openalex.org/W2163922914","https://openalex.org/W2165698076","https://openalex.org/W2165701072","https://openalex.org/W2250879510","https://openalex.org/W2335888457","https://openalex.org/W2340893350","https://openalex.org/W2344803892","https://openalex.org/W2400648064","https://openalex.org/W2515219719","https://openalex.org/W2550819555","https://openalex.org/W2568035689","https://openalex.org/W2576070019","https://openalex.org/W2582561810","https://openalex.org/W2595521492","https://openalex.org/W2619422284","https://openalex.org/W2746620120","https://openalex.org/W2753692828","https://openalex.org/W2753920499","https://openalex.org/W2765143157","https://openalex.org/W2781765535","https://openalex.org/W2783033852","https://openalex.org/W2783564496","https://openalex.org/W2791544114","https://openalex.org/W2794255242","https://openalex.org/W2795226110","https://openalex.org/W2799040008","https://openalex.org/W2801305544","https://openalex.org/W2801786826","https://openalex.org/W2804172836","https://openalex.org/W2810255987","https://openalex.org/W2891322914","https://openalex.org/W2919115771","https://openalex.org/W2950577311","https://openalex.org/W2964294337","https://openalex.org/W3102867977","https://openalex.org/W3125182500","https://openalex.org/W3149959729","https://openalex.org/W4205947740","https://openalex.org/W4231926072","https://openalex.org/W4237291031","https://openalex.org/W6607169468","https://openalex.org/W6608114916","https://openalex.org/W6636510571","https://openalex.org/W6678885109","https://openalex.org/W6712460733","https://openalex.org/W6732151605","https://openalex.org/W6734560207","https://openalex.org/W6785680529"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W2939353110","https://openalex.org/W2126887587","https://openalex.org/W4312962853","https://openalex.org/W4230611425","https://openalex.org/W2948658236","https://openalex.org/W2941846814","https://openalex.org/W3118091236"],"abstract_inverted_index":{"Nowadays":[0],"social":[1,33,56,111,125,195],"networks":[2,34,57,112,126],"are":[3,35,98],"widespread":[4],"used":[5],"not":[6],"only":[7],"to":[8,11,25,38,48,62,85,115,133,145,169],"enable":[9],"users":[10,16,78],"share":[12],"comments":[13],"with":[14],"other":[15],"but":[17],"also":[18,74],"as":[19],"tool":[20],"from":[21,67,75,110,152,192],"which":[22],"is":[23,83,90],"possible":[24],"extract":[26],"knowledge.":[27],"As":[28],"a":[29,44,49,100,142,153,160,185],"matter":[30],"of":[31,80,156,162,188,202],"fact,":[32],"increasingly":[36],"considered":[37],"understand":[39,63],"the":[40,64,76,91,128,181,193],"opinion":[41,66,129],"trend":[42],"about":[43],"politician":[45],"or":[46],"related":[47],"certain":[50],"event":[51],"that":[52],"occurred:":[53],"in":[54,94,103,113,200],"general":[55],"have":[58],"been":[59],"proved":[60],"useful":[61],"public":[65],"both":[68],"governments":[69],"and":[70,149,165,175,204],"companies.":[71],"In":[72,119,137],"addition,":[73],"end":[77],"point":[79,155],"view":[81],"it":[82],"difficult":[84,132],"identify":[86],"real":[87,150],"contents.":[88,136],"This":[89],"reason":[92],"why":[93],"last":[95],"years":[96],"we":[97,140],"witnessing":[99],"growing":[101],"interest":[102],"tools":[104],"for":[105],"analyzing":[106],"big":[107],"data":[108],"gathered":[109,191],"order":[114],"find":[116],"common":[117],"opinions.":[118],"this":[120,138],"context,":[121],"content":[122],"polluters":[123],"on":[124,184],"make":[127],"mining":[130],"process":[131],"browse":[134],"valuable":[135],"paper":[139],"propose":[141],"method":[143,183],"aimed":[144],"discriminate":[146],"between":[147,173],"pollute":[148],"information":[151],"semantic":[154,171],"view.":[157],"We":[158,179],"exploit":[159],"combination":[161],"word":[163],"embedding":[164],"deep":[166],"learning":[167],"techniques":[168],"categorize":[170],"similarities":[172],"(pollute":[174],"real)":[176],"linguistic":[177],"sentences.":[178],"experiment":[180],"proposed":[182],"dataset":[186],"composed":[187],"real-world":[189],"sentences":[190],"Twitter":[194],"network":[196],"obtaining":[197],"interesting":[198],"results":[199],"terms":[201],"precision":[203],"recall.":[205]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}