{"id":"https://openalex.org/W7171525202","doi":"https://doi.org/10.48550/arxiv.2607.24191","title":"StanceFlip: A Comprehensive Multi-Dimensional Benchmark for Multimodal Conversational Stance Flipping Forecasting","display_name":"StanceFlip: A Comprehensive Multi-Dimensional Benchmark for Multimodal Conversational Stance Flipping Forecasting","publication_year":2026,"publication_date":"2026-07-27","ids":{"openalex":"https://openalex.org/W7171525202","doi":"https://doi.org/10.48550/arxiv.2607.24191"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.24191","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.24191","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,"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.2607.24191","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045973629","display_name":"Heyan Chai","orcid":"https://orcid.org/0000-0003-4470-9364"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chai, Heyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143788437","display_name":"Xin Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143798798","display_name":"Wenjie Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Wenjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043800136","display_name":"Jianyang Qin","orcid":"https://orcid.org/0000-0003-1444-430X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin, Jianyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143838582","display_name":"Chaoyang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Chaoyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143857517","display_name":"Lu Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Lu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143828224","display_name":"Hao Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5143841121","display_name":"Qing Liao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liao, Qing","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/T10028","display_name":"Topic Modeling","score":0.4936999976634979,"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/T10028","display_name":"Topic Modeling","score":0.4936999976634979,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.10840000212192535,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.0723000019788742,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/benchmark","display_name":"Benchmark (surveying)","score":0.7134000062942505},{"id":"https://openalex.org/keywords/conversation","display_name":"Conversation","score":0.6043000221252441},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5202999711036682},{"id":"https://openalex.org/keywords/language-understanding","display_name":"Language understanding","score":0.5062000155448914},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.46000000834465027},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.3646000027656555},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.3517000079154968},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.34470000863075256}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7731999754905701},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7134000062942505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6147000193595886},{"id":"https://openalex.org/C2777200299","wikidata":"https://www.wikidata.org/wiki/Q52943","display_name":"Conversation","level":2,"score":0.6043000221252441},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5202999711036682},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.5062000155448914},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4729999899864197},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.46000000834465027},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3646000027656555},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.3517000079154968},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.34470000863075256},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.34360000491142273},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3359000086784363},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.32019999623298645},{"id":"https://openalex.org/C45493050","wikidata":"https://www.wikidata.org/wiki/Q7884934","display_name":"Unified Model","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C313442","wikidata":"https://www.wikidata.org/wiki/Q778556","display_name":"Persona","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.27149999141693115},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.24191","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.24191","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,"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.2607.24191","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.24191","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,"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/4","score":0.4073699712753296,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Conversational":[0,137],"stance":[1,30,69,115,151,167,193],"detection":[2],"has":[3],"shifted":[4],"from":[5,37],"static":[6,99],"text":[7],"analysis":[8],"to":[9,21,104,183],"dynamic":[10,24],"multimodal":[11,47,67,213],"modeling.":[12],"However,":[13],"existing":[14],"benchmarks":[15],"exhibit":[16],"three":[17],"key":[18],"limitations:":[19],"failure":[20],"capture":[22,105],"the":[23,43,118,126,176],"evolution":[25],"of":[26,42,46,102],"beliefs,":[27],"particularly":[28],"during":[29],"reversals;":[31],"difficulty":[32],"in":[33,49],"disentangling":[34],"affective":[35],"states":[36],"logical":[38],"reasoning;":[39],"and":[40,78,96,120,159,169,190,208],"neglect":[41],"critical":[44],"role":[45],"cues":[48],"resolving":[50],"pragmatic":[51],"ambiguities":[52],"such":[53],"as":[54,98],"sarcasm.":[55],"To":[56],"address":[57],"these":[58],"limitations,":[59],"we":[60,128],"propose":[61,129],"StanceFlip,":[62],"a":[63,130,144,154,160],"benchmark":[64],"designed":[65],"for":[66,135,165],"conversational":[68],"flipping":[70],"forecasting":[71],"over":[72],"multi-turn":[73],"dialogues":[74],"across":[75,117],"five":[76],"modalities":[77],"multi-scenarios,":[79],"which":[80],"includes":[81],"two":[82],"novel":[83],"subtasks:":[84],"1)":[85],"Multimodal":[86,136],"Stance":[87,111,138],"Sextuple":[88],"Extraction,":[89],"extracting":[90],"holder,":[91],"target,":[92],"emotion,":[93],"sentiment,":[94],"stance,":[95],"rationale":[97],"state":[100],"snapshots":[101],"dialogue":[103],"fine-grained":[106],"cognitive":[107,181],"structures.":[108],"2)":[109],"Dynamic":[110],"Flip":[112],"Attribution,":[113],"tracking":[114],"reversals":[116],"conversation":[119],"identifying":[121],"their":[122],"underlying":[123],"triggers.":[124],"Alongside":[125],"dataset,":[127],"dedicated":[131],"framework,":[132],"named":[133],"ConStaFF,":[134],"Flipping":[139],"Forecasting":[140],"(MCSFF).":[141],"Built":[142],"upon":[143],"large":[145,214],"language":[146,215],"model,":[147],"ConStaFF":[148],"performs":[149],"end-to-end":[150],"reasoning,":[152],"with":[153],"Thought-of-Stance":[155],"(ToS)":[156],"reasoning":[157,177],"framework":[158],"self-reflective":[161],"verification":[162],"mechanism":[163],"integrated":[164],"structured":[166],"modeling":[168],"faithful":[170],"flip":[171],"attribution.":[172],"Specifically,":[173],"ToS":[174],"decomposes":[175],"process":[178],"into":[179],"specialized":[180],"personas":[182],"formulate":[184],"target":[185],"propositions,":[186],"resolve":[187],"cross-modal":[188],"conflicts,":[189],"infer":[191],"historical":[192],"trajectories.":[194],"Extensive":[195],"experiments":[196],"show":[197],"that":[198],"our":[199],"approach":[200],"achieves":[201],"state-of-the-art":[202],"performance":[203],"on":[204],"both":[205],"sextuple":[206],"extraction":[207],"flip-trigger":[209],"attribution,":[210],"outperforming":[211],"strong":[212],"model":[216],"baselines":[217],"by":[218],"substantial":[219],"margins.":[220]},"counts_by_year":[],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2026-07-29T00:00:00"}