{"id":"https://openalex.org/W4412889898","doi":"https://doi.org/10.18653/v1/2025.acl-long.1217","title":"iNews: A Multimodal Dataset for Modeling Personalized Affective Responses to News","display_name":"iNews: A Multimodal Dataset for Modeling Personalized Affective Responses to News","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412889898","doi":"https://doi.org/10.18653/v1/2025.acl-long.1217"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2025.acl-long.1217","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.1217","pdf_url":"https://aclanthology.org/2025.acl-long.1217.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":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2025.acl-long.1217.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5006591088","display_name":"Tiancheng Hu","orcid":"https://orcid.org/0009-0006-7354-1088"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]},{"id":"https://openalex.org/I4210096386","display_name":"Bridge University","ror":"https://ror.org/00cbm0437","country_code":"SS","type":"education","lineage":["https://openalex.org/I4210096386"]}],"countries":["GB","SS"],"is_corresponding":false,"raw_author_name":"Tiancheng Hu","raw_affiliation_strings":["University of Cambridge University of Cambridge"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Cambridge University of Cambridge","institution_ids":["https://openalex.org/I241749","https://openalex.org/I4210096386"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073413742","display_name":"Nigel Collier","orcid":"https://orcid.org/0000-0002-7230-4164"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]},{"id":"https://openalex.org/I4210096386","display_name":"Bridge University","ror":"https://ror.org/00cbm0437","country_code":"SS","type":"education","lineage":["https://openalex.org/I4210096386"]}],"countries":["GB","SS"],"is_corresponding":false,"raw_author_name":"Nigel Collier","raw_affiliation_strings":["University of Cambridge University of Cambridge"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Cambridge University of Cambridge","institution_ids":["https://openalex.org/I241749","https://openalex.org/I4210096386"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"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":0.17111771,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"25000","last_page":"25040"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.9135000109672546,"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"}},"topics":[{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.9135000109672546,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6904143691062927},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4246748089790344},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3465009331703186},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3333319425582886},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.32271355390548706},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.12305739521980286}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6904143691062927},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4246748089790344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3465009331703186},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3333319425582886},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.32271355390548706},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.12305739521980286}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2025.acl-long.1217","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.1217","pdf_url":"https://aclanthology.org/2025.acl-long.1217.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":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2025.acl-long.1217","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2025.acl-long.1217","pdf_url":"https://aclanthology.org/2025.acl-long.1217.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":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Gender equality","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/5"}],"awards":[{"id":"https://openalex.org/G1690597291","display_name":null,"funder_award_id":"#OPP1144","funder_id":"https://openalex.org/F4320323264","funder_display_name":"Gates Cambridge Trust"},{"id":"https://openalex.org/G5477836236","display_name":"Cambridge University Development Office in the United States Inc (CUDOUS) - to endow a global scholarship program at the post-baccalaureate level for academically gifted students","funder_award_id":"OPP1144","funder_id":"https://openalex.org/F4320306137","funder_display_name":"Bill and Melinda Gates Foundation"}],"funders":[{"id":"https://openalex.org/F4320306137","display_name":"Bill and Melinda Gates Foundation","ror":"https://ror.org/0456r8d26"},{"id":"https://openalex.org/F4320321848","display_name":"Cambridge Trust","ror":"https://ror.org/05sprqy15"},{"id":"https://openalex.org/F4320323264","display_name":"Gates Cambridge Trust","ror":"https://ror.org/033sn5p83"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412889898.pdf","grobid_xml":"https://content.openalex.org/works/W4412889898.grobid-xml"},"referenced_works_count":1,"referenced_works":["https://openalex.org/W3117550730"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Understanding":[0],"how":[1],"individuals":[2],"perceive":[3],"and":[4,13,16,110,125,150],"react":[5],"to":[6,28,55,63],"information":[7,119,141],"is":[8],"fundamental":[9],"for":[10],"advancing":[11],"social":[12],"behavioral":[14],"sciences":[15],"developing":[17],"human-centered":[18],"AI":[19],"systems.Current":[20],"approaches":[21],"often":[22],"lack":[23],"the":[24,40,57],"granular":[25],"data":[26],"needed":[27],"model":[29],"these":[30],"personalized":[31,60],"responses,":[32],"relying":[33],"instead":[34],"on":[35],"aggregated":[36],"labels":[37,98],"that":[38],"obscure":[39],"rich":[41],"variability":[42],"driven":[43],"by":[44],"individual":[45],"differences.We":[46],"introduce":[47],"iNews,":[48],"a":[49,143],"novel":[50],"large-scale":[51],"dataset":[52,66],"specifically":[53],"designed":[54],"facilitate":[56],"modeling":[58],"of":[59,88,131],"affective":[61],"responses":[62],"news":[64,79],"content.Our":[65],"comprises":[67],"annotations":[68],"from":[69,81],"291":[70],"demographically":[71],"diverse":[72],"UK":[73,83],"participants":[74],"across":[75],"2,899":[76],"multimodal":[77],"Facebook":[78],"posts":[80],"major":[82],"outlets,":[84],"with":[85,154],"an":[86],"average":[87],"5.18":[89],"annotators":[90,95],"per":[91],"sample.For":[92],"each":[93],"post,":[94],"provide":[96],"multifaceted":[97],"including":[99],"valence,":[100],"arousal,":[101],"dominance,":[102],"discrete":[103],"emotions,":[104],"content":[105],"relevance":[106],"judgments,":[107],"sharing":[108],"likelihood,":[109],"modality":[111],"importance":[112],"ratings.Crucially,":[113],"we":[114],"collect":[115],"comprehensive":[116],"annotator":[117],"persona":[118],"covering":[120],"demographics,":[121],"personality,":[122],"media":[123],"trust,":[124],"consumption":[126],"patterns,":[127],"which":[128],"explain":[129],"15.2%":[130],"annotation":[132],"variance":[133],"-substantially":[134],"higher":[135],"than":[136],"existing":[137],"NLP":[138],"datasets.Incorporating":[139],"this":[140],"yields":[142],"7%":[144],"accuracy":[145],"gain":[146],"in":[147],"zero-shot":[148],"prediction":[149],"remains":[151],"beneficial":[152],"even":[153],"32-shot":[155],"in-context":[156],"learning.":[157]},"counts_by_year":[],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}