{"id":"https://openalex.org/W4412888957","doi":"https://doi.org/10.18653/v1/2025.findings-acl.32","title":"Talking Point based Ideological Discourse Analysis in News Events","display_name":"Talking Point based Ideological Discourse Analysis in News Events","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412888957","doi":"https://doi.org/10.18653/v1/2025.findings-acl.32"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2025.findings-acl.32","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-acl.32","pdf_url":"https://aclanthology.org/2025.findings-acl.32.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2025","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.findings-acl.32.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088916175","display_name":"Nishanth Sridhar Nakshatri","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nishanth Sridhar Nakshatri","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078550327","display_name":"Nikhil Mehta","orcid":"https://orcid.org/0000-0002-7501-9975"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nikhil Mehta","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100769981","display_name":"Siyi Liu","orcid":"https://orcid.org/0000-0001-7352-070X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Siyi Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101823158","display_name":"Sihao Chen","orcid":"https://orcid.org/0000-0001-8416-4261"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sihao Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022691777","display_name":"Daniel J. Hopkins","orcid":"https://orcid.org/0000-0002-9085-6050"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daniel Hopkins","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023802054","display_name":"Dan Roth","orcid":"https://orcid.org/0009-0002-1447-5173"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dan Roth","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5032121234","display_name":"Dan Goldwasser","orcid":"https://orcid.org/0000-0001-9326-8601"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dan Goldwasser","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08639382,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"575","last_page":"594"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.5713000297546387,"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":0.5713000297546387,"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/T11121","display_name":"Public Relations and Crisis Communication","score":0.4864000082015991,"subfield":{"id":"https://openalex.org/subfields/3315","display_name":"Communication"},"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/ideology","display_name":"Ideology","score":0.7187684774398804},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6133717894554138},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.6078536510467529},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.3682865500450134},{"id":"https://openalex.org/keywords/political-science","display_name":"Political science","score":0.19112280011177063},{"id":"https://openalex.org/keywords/politics","display_name":"Politics","score":0.1463514268398285},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09242329001426697},{"id":"https://openalex.org/keywords/philosophy","display_name":"Philosophy","score":0.08770009875297546}],"concepts":[{"id":"https://openalex.org/C158071213","wikidata":"https://www.wikidata.org/wiki/Q7257","display_name":"Ideology","level":3,"score":0.7187684774398804},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6133717894554138},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.6078536510467529},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.3682865500450134},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.19112280011177063},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.1463514268398285},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09242329001426697},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.08770009875297546},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.findings-acl.32","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-acl.32","pdf_url":"https://aclanthology.org/2025.findings-acl.32.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2025","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.findings-acl.32","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.findings-acl.32","pdf_url":"https://aclanthology.org/2025.findings-acl.32.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2653445185","display_name":"Collaborative Research: III: Small: Robust Learning and Inference Protocols for Mitigating Information Pollution","funder_award_id":"2135573","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320312530","display_name":"Office of the Director of National Intelligence","ror":"https://ror.org/01v3fsc55"},{"id":"https://openalex.org/F4320333051","display_name":"Intelligence Advanced Research Projects Activity","ror":"https://ror.org/01v3fsc55"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412888957.pdf","grobid_xml":"https://content.openalex.org/works/W4412888957.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2358556841","https://openalex.org/W2393918707","https://openalex.org/W2347506984","https://openalex.org/W2356608624","https://openalex.org/W2389874476","https://openalex.org/W2357886583","https://openalex.org/W2948332946"],"abstract_inverted_index":{"Analyzing":[0],"ideological":[1,48,62],"discourse":[2,63],"even":[3],"in":[4,145],"the":[5,19,38,59,75,85,162],"age":[6],"of":[7,61,98,104,142,152],"LLMs":[8,26],"remains":[9],"a":[10,55,79,96,102,149],"challenge,":[11],"as":[12],"these":[13,51,125],"models":[14],"often":[15],"struggle":[16],"to":[17,28,40,65,70,112,123,161,164],"capture":[18],"key":[20],"elements":[21,32],"that":[22,109],"shape":[23],"real-world":[24,71],"narratives.Specifically,":[25],"fail":[27],"focus":[29],"on":[30],"characteristic":[31],"driving":[33],"dominant":[34],"discourses":[35],"and":[36,91,130,159],"lack":[37],"ability":[39,122],"integrate":[41],"contextual":[42],"information":[43],"required":[44],"for":[45],"understanding":[46],"abstract":[47],"views.To":[49],"address":[50],"limitations,":[52],"we":[53,138],"propose":[54],"framework":[56,73,144],"motivated":[57],"by":[58,135],"theory":[60],"analysis":[64],"analyze":[66],"news":[67,76],"articles":[68,77],"related":[69],"events.Our":[72],"represents":[74],"using":[78],"relational":[80],"structure-talking":[81],"points,":[82,108],"which":[83],"captures":[84],"interaction":[86],"between":[87],"entities,":[88],"their":[89],"roles,":[90],"media":[92],"frames":[93],"along":[94],"with":[95],"topic":[97],"discussion.It":[99],"then":[100],"constructs":[101],"vocabulary":[103],"repeating":[105],"themes-prominent":[106],"talking":[107],"are":[110],"used":[111],"generate":[113,124],"ideology-specific":[114],"viewpoints":[115],"(or":[116],"partisan":[117,131],"perspectives).We":[118],"evaluate":[119],"our":[120,143],"framework's":[121],"perspectives":[126],"through":[127],"automated":[128],"tasks-ideology":[129],"classification":[132],"tasks,":[133],"supplemented":[134],"human":[136],"validation.Additionally,":[137],"demonstrate":[139],"straightforward":[140],"applicability":[141],"creating":[146],"event":[147,154],"snapshots,":[148],"visual":[150],"way":[151],"interpreting":[153],"discourse.We":[155],"release":[156],"resulting":[157],"dataset":[158],"model":[160],"community":[163],"support":[165],"further":[166],"research":[167],"1":[168],".":[169]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}