{"id":"https://openalex.org/W4210410782","doi":"https://doi.org/10.1109/tnnls.2021.3137045","title":"MGEL: Multigrained Representation Analysis and Ensemble Learning for Text Moderation","display_name":"MGEL: Multigrained Representation Analysis and Ensemble Learning for Text Moderation","publication_year":2022,"publication_date":"2022-02-03","ids":{"openalex":"https://openalex.org/W4210410782","doi":"https://doi.org/10.1109/tnnls.2021.3137045","pmid":"https://pubmed.ncbi.nlm.nih.gov/35113788"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2021.3137045","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2021.3137045","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5100740041","display_name":"Fei Tan","orcid":"https://orcid.org/0000-0002-3232-1912"},"institutions":[{"id":"https://openalex.org/I4210134091","display_name":"Yahoo (United States)","ror":"https://ror.org/040dkzz12","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134091"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fei Tan","raw_affiliation_strings":["Yahoo Research, New York, NY, USA"],"raw_orcid":"https://orcid.org/0000-0002-3232-1912","affiliations":[{"raw_affiliation_string":"Yahoo Research, New York, NY, USA","institution_ids":["https://openalex.org/I4210134091"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048671077","display_name":"Changwei Hu","orcid":"https://orcid.org/0000-0002-4094-6605"},"institutions":[{"id":"https://openalex.org/I4210134091","display_name":"Yahoo (United States)","ror":"https://ror.org/040dkzz12","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134091"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Changwei Hu","raw_affiliation_strings":["Yahoo Research, New York, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yahoo Research, New York, NY, USA","institution_ids":["https://openalex.org/I4210134091"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100708726","display_name":"Yifan Hu","orcid":"https://orcid.org/0000-0003-2017-924X"},"institutions":[{"id":"https://openalex.org/I4210134091","display_name":"Yahoo (United States)","ror":"https://ror.org/040dkzz12","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134091"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yifan Hu","raw_affiliation_strings":["Yahoo Research, New York, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yahoo Research, New York, NY, USA","institution_ids":["https://openalex.org/I4210134091"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073639119","display_name":"Kevin Yen","orcid":"https://orcid.org/0000-0002-4338-9680"},"institutions":[{"id":"https://openalex.org/I4210134091","display_name":"Yahoo (United States)","ror":"https://ror.org/040dkzz12","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134091"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kevin Yen","raw_affiliation_strings":["Yahoo Research, New York, NY, USA"],"raw_orcid":"https://orcid.org/0000-0002-4338-9680","affiliations":[{"raw_affiliation_string":"Yahoo Research, New York, NY, USA","institution_ids":["https://openalex.org/I4210134091"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001916237","display_name":"Zhi Wei","orcid":"https://orcid.org/0000-0001-6059-4267"},"institutions":[{"id":"https://openalex.org/I118118575","display_name":"New Jersey Institute of Technology","ror":"https://ror.org/05e74xb87","country_code":"US","type":"education","lineage":["https://openalex.org/I118118575"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhi Wei","raw_affiliation_strings":["Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA"],"raw_orcid":"https://orcid.org/0000-0001-6059-4267","affiliations":[{"raw_affiliation_string":"Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA","institution_ids":["https://openalex.org/I118118575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113662152","display_name":"Aasish Pappu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aasish Pappu","raw_affiliation_strings":["Spotify Research, New York, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Spotify Research, New York, NY, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007855897","display_name":"Serim Park","orcid":"https://orcid.org/0009-0004-0131-245X"},"institutions":[{"id":"https://openalex.org/I113979032","display_name":"Twitter (United States)","ror":"https://ror.org/04wt43v05","country_code":"US","type":"company","lineage":["https://openalex.org/I113979032"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Serim Park","raw_affiliation_strings":["Twitter, San Francisco, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Twitter, San Francisco, CA, USA","institution_ids":["https://openalex.org/I113979032"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049950372","display_name":"Keqian Li","orcid":"https://orcid.org/0009-0002-5956-3038"},"institutions":[{"id":"https://openalex.org/I4210134091","display_name":"Yahoo (United States)","ror":"https://ror.org/040dkzz12","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134091"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Keqian Li","raw_affiliation_strings":["Yahoo Research, New York, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yahoo Research, New York, NY, USA","institution_ids":["https://openalex.org/I4210134091"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4638,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.66930292,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"34","issue":"10","first_page":"7014","last_page":"7023"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":0.9998000264167786,"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/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":0.9998000264167786,"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":0.8074811100959778},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5508296489715576},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4873822331428528},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4538572430610657},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.4295303225517273},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4234105348587036},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35361915826797485},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.11881497502326965}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8074811100959778},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5508296489715576},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4873822331428528},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4538572430610657},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4295303225517273},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4234105348587036},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35361915826797485},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.11881497502326965},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2021.3137045","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2021.3137045","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:35113788","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35113788","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5199999809265137,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W78136081","https://openalex.org/W1614298861","https://openalex.org/W1797580880","https://openalex.org/W1924770834","https://openalex.org/W1976526581","https://openalex.org/W2082983083","https://openalex.org/W2119769989","https://openalex.org/W2147717514","https://openalex.org/W2154359981","https://openalex.org/W2160685721","https://openalex.org/W2170240176","https://openalex.org/W2250539671","https://openalex.org/W2340954483","https://openalex.org/W2401080074","https://openalex.org/W2493916176","https://openalex.org/W2613977835","https://openalex.org/W2621167245","https://openalex.org/W2740168486","https://openalex.org/W2741065173","https://openalex.org/W2760103715","https://openalex.org/W2806872289","https://openalex.org/W2889016530","https://openalex.org/W2896457183","https://openalex.org/W2922580172","https://openalex.org/W2938830017","https://openalex.org/W2951559648","https://openalex.org/W2962784628","https://openalex.org/W2962797668","https://openalex.org/W2963208801","https://openalex.org/W2963626623","https://openalex.org/W2963793818","https://openalex.org/W2963979492","https://openalex.org/W2964046515","https://openalex.org/W2965373594","https://openalex.org/W2973034686","https://openalex.org/W2998030657","https://openalex.org/W2998353611","https://openalex.org/W3034951181","https://openalex.org/W3101785758","https://openalex.org/W3103214757","https://openalex.org/W3122907670","https://openalex.org/W3156594246","https://openalex.org/W3201425170","https://openalex.org/W6603174692","https://openalex.org/W6636510571","https://openalex.org/W6640212811","https://openalex.org/W6678162470","https://openalex.org/W6682839988","https://openalex.org/W6685053522","https://openalex.org/W6687056112","https://openalex.org/W6713380983","https://openalex.org/W6755207826","https://openalex.org/W6761910064","https://openalex.org/W6766673545","https://openalex.org/W6766869486"],"related_works":["https://openalex.org/W4390516098","https://openalex.org/W2181948922","https://openalex.org/W2384362569","https://openalex.org/W4205302943","https://openalex.org/W2119949815","https://openalex.org/W2561132942","https://openalex.org/W2142795561","https://openalex.org/W3155418658","https://openalex.org/W4243199227","https://openalex.org/W2944691285"],"abstract_inverted_index":{"In":[0,58],"this":[1,123,133],"work,":[2],"we":[3,60,93,125],"describe":[4],"our":[5,155],"efforts":[6],"in":[7,13,111],"addressing":[8],"two":[9],"typical":[10],"challenges":[11],"involved":[12],"the":[14,26,44,52,74,97,143,147],"popular":[15],"text":[16,24,91],"classification":[17],"methods":[18],"when":[19],"they":[20],"are":[21],"applied":[22],"to":[23,41,72,95,166],"moderation:":[25],"representation":[27],"of":[28,46,54],"multibyte":[29],"characters":[30],"and":[31,131,138,163],"word":[32,119,168],"obfuscations.":[33,120,169],"Specifically,":[34],"a":[35,62,87,161],"multihot":[36],"byte-level":[37],"scheme":[38],"is":[39],"developed":[40],"significantly":[42],"reduce":[43],"dimension":[45],"one-hot":[47],"character-level":[48],"encoding":[49],"caused":[50],"by":[51,135,154],"multiplicity":[53],"instance-scarce":[55],"non-ASCII":[56],"characters.":[57],"addition,":[59],"introduce":[61],"simple":[63],"yet":[64],"effective":[65,164],"weighting":[66],"approach":[67],"for":[68],"fusing":[69],"n-gram":[70],"features":[71],"empower":[73],"classical":[75],"logistic":[76],"regression.":[77],"Surprisingly,":[78],"it":[79],"outperforms":[80],"well-tuned":[81],"representative":[82],"neural":[83],"networks":[84],"greatly.":[85],"As":[86],"continual":[88],"effort":[89],"toward":[90],"moderation,":[92],"endeavor":[94],"analyze":[96],"current":[98],"state-of-the-art":[99],"(SOTA)":[100],"algorithm":[101],"bidirectional":[102],"encoder":[103],"representations":[104],"from":[105],"transformers":[106],"(BERT),":[107],"which":[108],"works":[109],"well":[110],"context":[112],"understanding":[113],"but":[114],"performs":[115],"poorly":[116],"on":[117,146],"intentional":[118],"To":[121],"resolve":[122],"crux,":[124],"then":[126],"develop":[127],"an":[128],"enhanced":[129],"variant":[130],"remedy":[132],"drawback":[134],"integrating":[136],"byte":[137],"character":[139],"decomposition.":[140],"It":[141],"advances":[142],"SOTA":[144],"performance":[145],"largest":[148],"abusive":[149],"language":[150],"datasets":[151],"as":[152],"demonstrated":[153],"comprehensive":[156],"experiments.":[157],"Our":[158],"work":[159],"offers":[160],"feasible":[162],"framework":[165],"tackle":[167]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}