{"id":"https://openalex.org/W4403791291","doi":"https://doi.org/10.1145/3664647.3681416","title":"Multimodal Multi-turn Conversation Stance Detection: A Challenge Dataset and Effective Model","display_name":"Multimodal Multi-turn Conversation Stance Detection: A Challenge Dataset and Effective Model","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4403791291","doi":"https://doi.org/10.1145/3664647.3681416"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3681416","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3681416","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"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/A5063173209","display_name":"Fuqiang Niu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fuqiang Niu","raw_affiliation_strings":["Shenzhen Technology University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0004-0502-6960","affiliations":[{"raw_affiliation_string":"Shenzhen Technology University, Shenzhen, China","institution_ids":["https://openalex.org/I4210152380"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053952600","display_name":"Zebang Cheng","orcid":"https://orcid.org/0009-0001-2854-7425"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zebang Cheng","raw_affiliation_strings":["Shenzhen Technology University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0001-2854-7425","affiliations":[{"raw_affiliation_string":"Shenzhen Technology University, Shenzhen, China","institution_ids":["https://openalex.org/I4210152380"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111973980","display_name":"Xianghua Fu","orcid":"https://orcid.org/0009-0009-0951-3199"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianghua Fu","raw_affiliation_strings":["Shenzhen Technology University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0009-0951-3199","affiliations":[{"raw_affiliation_string":"Shenzhen Technology University, Shenzhen, China","institution_ids":["https://openalex.org/I4210152380"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033413473","display_name":"Xiaojiang Peng","orcid":"https://orcid.org/0000-0002-5783-321X"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaojiang Peng","raw_affiliation_strings":["Shenzhen Technology University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-5783-321X","affiliations":[{"raw_affiliation_string":"Shenzhen Technology University, Shenzhen, China","institution_ids":["https://openalex.org/I4210152380"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018994926","display_name":"Genan Dai","orcid":"https://orcid.org/0000-0003-2583-0433"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Genan Dai","raw_affiliation_strings":["Shenzhen Technology University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-2583-0433","affiliations":[{"raw_affiliation_string":"Shenzhen Technology University, Shenzhen, China","institution_ids":["https://openalex.org/I4210152380"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100430547","display_name":"Yin Chen","orcid":"https://orcid.org/0000-0001-5389-2821"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yin Chen","raw_affiliation_strings":["Shenzhen Technology University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-5389-2821","affiliations":[{"raw_affiliation_string":"Shenzhen Technology University, Shenzhen, China","institution_ids":["https://openalex.org/I4210152380"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075800064","display_name":"Hu Huang","orcid":"https://orcid.org/0009-0005-9674-258X"},"institutions":[{"id":"https://openalex.org/I4210128628","display_name":"Peking University Shenzhen Hospital","ror":"https://ror.org/03kkjyb15","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210128628"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hu Huang","raw_affiliation_strings":["Peking University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0005-9674-258X","affiliations":[{"raw_affiliation_string":"Peking University, Shenzhen, China","institution_ids":["https://openalex.org/I4210128628"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100385138","display_name":"Bowen Zhang","orcid":"https://orcid.org/0000-0002-3581-9476"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bowen Zhang","raw_affiliation_strings":["Shenzhen Technology University, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-3581-9476","affiliations":[{"raw_affiliation_string":"Shenzhen Technology University, Shenzhen, China","institution_ids":["https://openalex.org/I4210152380"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3867","last_page":"3876"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11148","display_name":"Language, Metaphor, and Cognition","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11148","display_name":"Language, Metaphor, and Cognition","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9939000010490417,"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/T12031","display_name":"Speech and dialogue systems","score":0.9833999872207642,"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/conversation","display_name":"Conversation","score":0.8801334500312805},{"id":"https://openalex.org/keywords/turn-taking","display_name":"Turn-taking","score":0.7387298345565796},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.676659345626831},{"id":"https://openalex.org/keywords/turn","display_name":"Turn (biochemistry)","score":0.5000548362731934},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4094161093235016},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33150893449783325},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.10699933767318726},{"id":"https://openalex.org/keywords/communication","display_name":"Communication","score":0.10259479284286499}],"concepts":[{"id":"https://openalex.org/C2777200299","wikidata":"https://www.wikidata.org/wiki/Q52943","display_name":"Conversation","level":2,"score":0.8801334500312805},{"id":"https://openalex.org/C2776352735","wikidata":"https://www.wikidata.org/wiki/Q2313343","display_name":"Turn-taking","level":3,"score":0.7387298345565796},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.676659345626831},{"id":"https://openalex.org/C85641259","wikidata":"https://www.wikidata.org/wiki/Q290042","display_name":"Turn (biochemistry)","level":2,"score":0.5000548362731934},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4094161093235016},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33150893449783325},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.10699933767318726},{"id":"https://openalex.org/C46312422","wikidata":"https://www.wikidata.org/wiki/Q11024","display_name":"Communication","level":1,"score":0.10259479284286499},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3664647.3681416","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3681416","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2347127863","https://openalex.org/W2460159515","https://openalex.org/W2565166462","https://openalex.org/W2788967885","https://openalex.org/W2892181857","https://openalex.org/W2912018209","https://openalex.org/W2953873732","https://openalex.org/W2963811339","https://openalex.org/W3004975108","https://openalex.org/W3034524234","https://openalex.org/W3154430186","https://openalex.org/W3173991475","https://openalex.org/W4224311791","https://openalex.org/W4284677576","https://openalex.org/W4360612314","https://openalex.org/W4372266939","https://openalex.org/W4386076522","https://openalex.org/W4389524530","https://openalex.org/W4392909948","https://openalex.org/W4401042533"],"related_works":["https://openalex.org/W2900127165","https://openalex.org/W320957374","https://openalex.org/W2379107843","https://openalex.org/W3120641923","https://openalex.org/W2228992124","https://openalex.org/W2961085424","https://openalex.org/W2613954729","https://openalex.org/W2909654650","https://openalex.org/W3161619631","https://openalex.org/W4306674287"],"abstract_inverted_index":{"Stance":[0],"detection,":[1],"which":[2],"aims":[3],"to":[4,152],"identify":[5],"public":[6],"opinion":[7],"towards":[8],"specific":[9],"targets":[10],"using":[11],"social":[12,27,66],"media":[13,28],"data,":[14],"is":[15],"an":[16],"important":[17],"yet":[18],"challenging":[19,107],"task.":[20],"With":[21],"the":[22,58],"proliferation":[23],"of":[24,74,137,156],"diverse":[25],"multimodal":[26,34,94,113,143],"content":[29],"including":[30],"text,":[31],"and":[32,128],"images":[33],"stance":[35,52,97,117,124,144,157],"detection":[36,98,118,158],"(MSD)":[37],"has":[38],"become":[39],"a":[40,72,92,111],"crucial":[41],"research":[42],"area.":[43],"However,":[44],"existing":[45],"MSD":[46],"studies":[47],"have":[48],"focused":[49],"on":[50,65,132],"modeling":[51],"within":[53],"individual":[54],"text-image":[55],"pairs,":[56],"overlooking":[57],"multi-party":[59],"conversational":[60,80,85,96],"contexts":[61],"that":[62,76,121,148],"naturally":[63],"occur":[64],"media.":[67],"This":[68],"limitation":[69],"stems":[70],"from":[71,105,126],"lack":[73],"datasets":[75],"authentically":[77],"capture":[78],"such":[79],"scenarios,":[81],"hindering":[82],"progress":[83],"in":[84],"MSD.":[86],"To":[87,102],"address":[88],"this,":[89],"we":[90,109],"introduce":[91],"new":[93],"multi-turn":[95],"dataset":[99],"(called":[100],"MmMtCSD).":[101],"derive":[103],"stances":[104],"this":[106],"dataset,":[108],"propose":[110],"novel":[112],"large":[114],"language":[115],"model":[116],"framework":[119],"(MLLM-SD),":[120],"learns":[122],"joint":[123],"representations":[125],"textual":[127],"visual":[129],"modalities.":[130],"Experiments":[131],"MmMtCSD":[133,149],"show":[134],"state-of-the-art":[135],"performance":[136],"our":[138],"proposed":[139],"MLLM-SD":[140],"approach":[141],"for":[142],"detection.":[145],"We":[146],"believe":[147],"will":[150],"contribute":[151],"advancing":[153],"real-world":[154],"applications":[155],"research.":[159]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}