{"id":"https://openalex.org/W3155286175","doi":null,"title":"Detecting Polarized Topics in COVID-19 News Using Partisanship-aware Contextualized Topic Embeddings","display_name":"Detecting Polarized Topics in COVID-19 News Using Partisanship-aware Contextualized Topic Embeddings","publication_year":2021,"publication_date":"2021-04-15","ids":{"openalex":"https://openalex.org/W3155286175","mag":"3155286175"},"language":"en","primary_location":{"id":"mag:3155286175","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2104.07814","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://export.arxiv.org/pdf/2104.07814","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"He Zhang","orcid":"https://orcid.org/0000-0001-9954-4451","observed_orcids":["https://orcid.org/0000-0001-9954-4451"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He Zhang","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-9954-4451","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060362045","display_name":"Negar Mokhberian","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Negar Mokhberian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109699292","display_name":"Amadou Sara Camara","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Amadou Sara Camara","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038897083","display_name":"Andr\u00e9s Abeliuk","orcid":"https://orcid.org/0000-0002-8158-4647","observed_orcids":["https://orcid.org/0000-0002-8158-4647"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andr\u00e9s Abeliuk","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-8158-4647","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5049634383","display_name":"Kristina Lerman","orcid":"https://orcid.org/0000-0002-5071-0575","observed_orcids":["https://orcid.org/0000-0002-5071-0575"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kristina Lerman","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-5071-0575","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":5,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.5942000150680542,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.5942000150680542,"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/T13910","display_name":"Computational and Text Analysis Methods","score":0.1370999962091446,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"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":0.13179999589920044,"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/partisanship","display_name":"partisanship","score":0.6290000081062317},{"id":"https://openalex.org/keywords/news-media","display_name":"news media","score":0.5210000276565552},{"id":"https://openalex.org/keywords/covid-19-news","display_name":"COVID-19 news","score":0.9409999847412109},{"id":"https://openalex.org/keywords/language-model-fine-tuning","display_name":"language model fine-tuning","score":0.6150000095367432}],"concepts":[{"id":"https://openalex.org/C205049153","wikidata":"https://www.wikidata.org/wiki/Q2698605","display_name":"Polarization (electrochemistry)","level":2,"score":0.791843056678772},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6345601081848145},{"id":"https://openalex.org/C529147693","wikidata":"https://www.wikidata.org/wiki/Q1193236","display_name":"News media","level":2,"score":0.5192410945892334},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.511207103729248},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.5061821341514587},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.4889041483402252},{"id":"https://openalex.org/C2779756789","wikidata":"https://www.wikidata.org/wiki/Q28549308","display_name":"Fake news","level":2,"score":0.4143708646297455},{"id":"https://openalex.org/C108827166","wikidata":"https://www.wikidata.org/wiki/Q175975","display_name":"Internet privacy","level":1,"score":0.33120298385620117},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3254506289958954},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.32112249732017517},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.28470396995544434},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.10682272911071777},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.10033115744590759},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C147789679","wikidata":"https://www.wikidata.org/wiki/Q11372","display_name":"Physical chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3155286175","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2104.07814","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:3155286175","doi":null,"is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2104.07814","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W561660722","https://openalex.org/W1714665356","https://openalex.org/W1880262756","https://openalex.org/W1954045752","https://openalex.org/W2006371035","https://openalex.org/W2038043464","https://openalex.org/W2102968466","https://openalex.org/W2117401803","https://openalex.org/W2119432837","https://openalex.org/W2137029365","https://openalex.org/W2250539671","https://openalex.org/W2305594859","https://openalex.org/W2626778328","https://openalex.org/W2950133940","https://openalex.org/W2963341956","https://openalex.org/W2963834345","https://openalex.org/W2965373594","https://openalex.org/W2971629656","https://openalex.org/W3008254539","https://openalex.org/W3015202090","https://openalex.org/W3027848310","https://openalex.org/W3036082643","https://openalex.org/W3037619012","https://openalex.org/W3081168372","https://openalex.org/W3088435213","https://openalex.org/W3091959351","https://openalex.org/W3097377734","https://openalex.org/W3099072156","https://openalex.org/W3100201982","https://openalex.org/W3101850370"],"related_works":["https://openalex.org/W3199564302","https://openalex.org/W3081168372","https://openalex.org/W4386903296","https://openalex.org/W4293247667","https://openalex.org/W4224290925","https://openalex.org/W4206347585","https://openalex.org/W3173406292","https://openalex.org/W4402169794","https://openalex.org/W7202014671","https://openalex.org/W4405547026"],"abstract_inverted_index":{"Growing":[0],"polarization":[1,35,98,135,151],"of":[2,18,34,69,91,111,127],"the":[3,67,92,97,125,133,150,153],"news":[4,62,71,93,112,120,140],"media":[5],"has":[6,85],"been":[7,86],"blamed":[8],"for":[9],"fanning":[10],"disagreement,":[11],"controversy":[12],"and":[13,95,122,136,145],"even":[14],"violence.":[15],"Early":[16],"identification":[17],"polarized":[19,58],"topics":[20,59,123],"is":[21,36],"thus":[22],"an":[23,38],"urgent":[24],"matter":[25],"that":[26,84],"can":[27],"help":[28,143],"mitigate":[29],"conflict.":[30],"However,":[31],"accurate":[32],"measurement":[33],"still":[37],"open":[39],"research":[40],"challenge.":[41],"To":[42,142],"address":[43],"this":[44],"gap,":[45],"we":[46,65,148],"propose":[47],"Partisanship-aware":[48],"Contextualized":[49],"Topic":[50],"Embeddings":[51],"(PaCTE),":[52],"a":[53,70,74,81,109],"method":[54,107,129],"to":[55,108,130],"automatically":[56],"detect":[57],"from":[60],"partisan":[61],"sources.":[63,141],"Specifically,":[64],"represent":[66],"ideology":[68],"source":[72],"on":[73,88,118],"topic":[75,78],"by":[76],"corpus-contextualized":[77],"embedding":[79],"utilizing":[80],"language":[82],"model":[83],"finetuned":[87],"recognizing":[89],"partisanship":[90],"articles,":[94],"measure":[96],"between":[99,138],"sources":[100,121],"using":[101,152],"cosine":[102],"similarity.":[103],"We":[104],"apply":[105],"our":[106,128],"corpus":[110],"about":[113],"COVID-19":[114],"pandemic.":[115],"Extensive":[116],"experiments":[117],"different":[119,139],"demonstrate":[124],"effectiveness":[126],"precisely":[131],"capture":[132],"topical":[134],"alignment":[137],"clarify":[144],"validate":[146],"results,":[147],"explain":[149],"Moral":[154],"Foundation":[155],"Theory.":[156]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2021-04-26T00:00:00"}