{"id":"https://openalex.org/W7166016353","doi":"https://doi.org/10.48550/arxiv.2606.26100","title":"HierBias: Context-Conditioned Hierarchical Media Bias Detection with Multi-Task Type Classification","display_name":"HierBias: Context-Conditioned Hierarchical Media Bias Detection with Multi-Task Type Classification","publication_year":2026,"publication_date":"2026-04-29","ids":{"openalex":"https://openalex.org/W7166016353","doi":"https://doi.org/10.48550/arxiv.2606.26100"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.26100","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26100","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.26100","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139455609","display_name":"Kaining Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Kaining","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139440566","display_name":"Ruichen Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Ruichen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136466952","display_name":"Y H Dong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Yuxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":0.3070000112056732,"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":0.3070000112056732,"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/T13629","display_name":"Text Readability and Simplification","score":0.1665000021457672,"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/T11147","display_name":"Misinformation and Its Impacts","score":0.13490000367164612,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4645000100135803},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4625999927520752},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.45410001277923584},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.44749999046325684},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.43389999866485596},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.3682999908924103},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.35530000925064087},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.33559998869895935}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6585999727249146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6331999897956848},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4645000100135803},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4625999927520752},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.45410001277923584},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.44749999046325684},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.43389999866485596},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.3682999908924103},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.35530000925064087},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.33559998869895935},{"id":"https://openalex.org/C2779190172","wikidata":"https://www.wikidata.org/wiki/Q4913888","display_name":"Binary data","level":3,"score":0.33550000190734863},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.32339999079704285},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31290000677108765},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.29490000009536743},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29269999265670776},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.28529998660087585},{"id":"https://openalex.org/C180505990","wikidata":"https://www.wikidata.org/wiki/Q498267","display_name":"News aggregator","level":2,"score":0.2840000092983246},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.2687000036239624},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26579999923706055},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C40696583","wikidata":"https://www.wikidata.org/wiki/Q989120","display_name":"Type I and type II errors","level":2,"score":0.25220000743865967},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.26100","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26100","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.26100","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26100","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.42718034982681274,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Media":[0],"bias":[1,38,46,52,85,89],"detection":[2,86,117],"is":[3,73],"a":[4,34,102,107],"critical":[5],"task":[6],"for":[7,115],"ensuring":[8],"fair":[9],"and":[10,54,87,111,118,125,131,141,158],"balanced":[11],"information":[12,72],"dissemination,":[13],"yet":[14],"existing":[15],"sentence-level":[16,67,103],"approaches":[17],"classify":[18],"each":[19,153],"sentence":[20],"independently,":[21],"ignoring":[22],"inter-sentence":[23,70],"contextual":[24],"signals":[25],"that":[26,40,57,81,152],"human":[27],"annotators":[28],"naturally":[29],"exploit.":[30],"We":[31,48],"present":[32],"\\textbf{HierBias},":[33],"hierarchical":[35],"context-conditioned":[36],"media":[37],"detector":[39],"formally":[41],"models":[42],"document":[43,59],"context":[44,60],"in":[45],"prediction.":[47],"introduce":[49],"the":[50,63,135],"\\emph{context-conditioned":[51],"probability}":[53],"prove":[55],"theoretically":[56],"leveraging":[58],"strictly":[61],"reduces":[62],"Bayes":[64],"error":[65],"of":[66],"classification":[68,91],"when":[69],"mutual":[71],"non-zero.":[74],"A":[75],"multi-task":[76],"generalization":[77],"bound":[78],"further":[79],"establishes":[80],"jointly":[82],"training":[83],"binary":[84,116],"fine-grained":[88],"type":[90,120],"improves":[92],"sample":[93],"efficiency":[94],"on":[95,123],"small":[96],"annotated":[97],"corpora.":[98],"Architecturally,":[99],"HierBias":[100,127],"pairs":[101],"RoBERTa":[104],"encoder":[105],"with":[106],"cross-sentence":[108],"Transformer":[109],"aggregator":[110],"dual":[112],"output":[113],"heads":[114],"four-class":[119],"classification.":[121],"Evaluated":[122],"BABE":[124],"BASIL,":[126],"achieves":[128],"0.853":[129],"F1":[130,140],"0.723":[132],"MCC,":[133],"surpassing":[134],"state-of-the-art":[136],"bias-detector":[137],"by":[138],"$+2.6\\%$":[139],"$+4.3\\%$":[142],"MCC":[143],"(McNemar's":[144],"test,":[145],"$p":[146],"&lt;":[147],"0.05$).":[148],"Ablation":[149],"experiments":[150],"confirm":[151],"theoretical":[154],"component":[155],"contributes":[156],"independently":[157],"consistently.":[159]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-27T00:00:00"}
