{"id":"https://openalex.org/W4416249732","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228993","title":"Stance Detection on Macro Topics in Social Discussions Via Multi-Factor Aggregation Analysis","display_name":"Stance Detection on Macro Topics in Social Discussions Via Multi-Factor Aggregation Analysis","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416249732","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228993"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228993","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228993","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","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/A5109535668","display_name":"Haihua Xie","orcid":"https://orcid.org/0000-0002-3357-4006"},"institutions":[{"id":"https://openalex.org/I4403928416","display_name":"Beijing Institute of Mathematical Sciences and Applications","ror":"https://ror.org/05t6hvr95","country_code":null,"type":"education","lineage":["https://openalex.org/I4403928416","https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haihua Xie","raw_affiliation_strings":["Beijing Institute of Mathematical Sciences and Applications,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Mathematical Sciences and Applications,Beijing,China","institution_ids":["https://openalex.org/I4403928416"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100621757","display_name":"Miao He","orcid":"https://orcid.org/0000-0003-0648-0773"},"institutions":[{"id":"https://openalex.org/I4403928416","display_name":"Beijing Institute of Mathematical Sciences and Applications","ror":"https://ror.org/05t6hvr95","country_code":null,"type":"education","lineage":["https://openalex.org/I4403928416","https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Miao He","raw_affiliation_strings":["Beijing Institute of Mathematical Sciences and Applications,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing Institute of Mathematical Sciences and Applications,Beijing,China","institution_ids":["https://openalex.org/I4403928416"]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5109535668"],"corresponding_institution_ids":["https://openalex.org/I4403928416"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.46604527,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.7073000073432922,"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":0.7073000073432922,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.09719999879598618,"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/T10028","display_name":"Topic Modeling","score":0.032499998807907104,"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/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.756600022315979},{"id":"https://openalex.org/keywords/macro","display_name":"Macro","score":0.741599977016449},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.4837000072002411},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.45649999380111694},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.43869999051094055},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.40400001406669617},{"id":"https://openalex.org/keywords/latent-semantic-analysis","display_name":"Latent semantic analysis","score":0.39910000562667847},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.38350000977516174},{"id":"https://openalex.org/keywords/thematic-analysis","display_name":"Thematic analysis","score":0.35179999470710754}],"concepts":[{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.756600022315979},{"id":"https://openalex.org/C166955791","wikidata":"https://www.wikidata.org/wiki/Q629579","display_name":"Macro","level":2,"score":0.741599977016449},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5532000064849854},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.4837000072002411},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.45649999380111694},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.43869999051094055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4255000054836273},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.40400001406669617},{"id":"https://openalex.org/C170133592","wikidata":"https://www.wikidata.org/wiki/Q1806883","display_name":"Latent semantic analysis","level":2,"score":0.39910000562667847},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.38350000977516174},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.3772999942302704},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3596000075340271},{"id":"https://openalex.org/C74196892","wikidata":"https://www.wikidata.org/wiki/Q7781188","display_name":"Thematic analysis","level":3,"score":0.35179999470710754},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.31450000405311584},{"id":"https://openalex.org/C93692415","wikidata":"https://www.wikidata.org/wiki/Q1502030","display_name":"Thematic map","level":2,"score":0.3125},{"id":"https://openalex.org/C84389358","wikidata":"https://www.wikidata.org/wiki/Q1129466","display_name":"Discourse analysis","level":2,"score":0.31220000982284546},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.2989000082015991},{"id":"https://openalex.org/C3017399102","wikidata":"https://www.wikidata.org/wiki/Q397254","display_name":"Macro level","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.2888999879360199},{"id":"https://openalex.org/C2778109090","wikidata":"https://www.wikidata.org/wiki/Q7781195","display_name":"Thematic structure","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.28040000796318054},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.27720001339912415},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.2759999930858612},{"id":"https://openalex.org/C2777946921","wikidata":"https://www.wikidata.org/wiki/Q7449044","display_name":"Semantic analysis (machine learning)","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.25999999046325684},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228993","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228993","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W75677969","https://openalex.org/W642125458","https://openalex.org/W2417560918","https://openalex.org/W2460159515","https://openalex.org/W2768226620","https://openalex.org/W2769529439","https://openalex.org/W2772223236","https://openalex.org/W2800534405","https://openalex.org/W2887804579","https://openalex.org/W2889964550","https://openalex.org/W2896457183","https://openalex.org/W2923280274","https://openalex.org/W2955015412","https://openalex.org/W2963277000","https://openalex.org/W3005971640","https://openalex.org/W3027173706","https://openalex.org/W3033229230","https://openalex.org/W3033317208","https://openalex.org/W3034524234","https://openalex.org/W3045889512","https://openalex.org/W3046111511","https://openalex.org/W3102743123","https://openalex.org/W3135161617","https://openalex.org/W3210631446","https://openalex.org/W4297054282","https://openalex.org/W4307570202","https://openalex.org/W4318344493","https://openalex.org/W4383503665","https://openalex.org/W4389042949"],"related_works":[],"abstract_inverted_index":{"Stance":[0],"detection\u2014the":[1],"task":[2],"of":[3,21,66,132,191],"inferring":[4],"users\u2019":[5],"implicit":[6,154],"attitudes":[7],"from":[8],"textual":[9],"statements\u2014plays":[10],"a":[11,76,89,144,199],"crucial":[12],"role":[13],"in":[14,135,179],"sociological":[15,204],"research":[16,205],"by":[17,177],"enabling":[18],"systematic":[19],"analysis":[20,131],"public":[22],"opinion":[23],"on":[24,55,164,183,206],"complex":[25],"societal":[26,210],"issues.":[27,211],"However,":[28],"existing":[29],"methods":[30,176],"struggle":[31],"with":[32,84,195],"macro":[33,98,141,158,184],"topics\u2014broad,":[34],"abstract":[35,207],"subjects":[36],"(e.g.,":[37,104],"\u2018globalization\u2019":[38],"or":[39,47],"\u2018AI":[40],"development\u2019)\u2014where":[41],"individuals":[42],"often":[43],"express":[44],"views":[45],"indirectly":[46],"avoid":[48],"explicit":[49],"statements.":[50],"Traditional":[51],"approaches,":[52],"which":[53],"rely":[54],"overt":[56],"linguistic":[57],"markers,":[58],"fail":[59],"to":[60,152],"capture":[61],"the":[62,121,140,157,189],"nuanced":[63],"discourse":[64,169],"characteristic":[65],"such":[67],"topics.":[68,185],"To":[69],"address":[70],"this":[71],"challenge,":[72],"we":[73],"propose":[74],"STS4MTS,":[75],"dual-layer":[77],"framework":[78],"that":[79,172],"integrates":[80],"latent":[81],"topic":[82,142,159,193],"modeling":[83],"hierarchical":[85],"semantic":[86,130],"analysis.":[87],"First,":[88],"discourse-driven":[90],"Discriminative":[91],"Latent":[92],"Dirichlet":[93],"Allocation":[94],"(Disc-LDA)":[95],"model":[96],"decomposes":[97],"topics":[99],"into":[100],"contextually":[101],"relevant":[102],"sub-topics":[103,151],"\u2018labor":[105],"market":[106],"impacts\u2019":[107],"under":[108],"\u2018globalization\u2019),":[109],"preserving":[110],"thematic":[111],"nuances.":[112],"Next,":[113],"stance":[114,148],"detection":[115],"is":[116],"performed":[117],"hierarchically:":[118],"(1)":[119],"at":[120,139],"sub-topic":[122],"level,":[123,143],"large":[124],"language":[125],"models":[126],"(LLMs)":[127],"conduct":[128],"fine-grained":[129],"stances":[133,182],"expressed":[134],"text,":[136],"and":[137,167,208],"(2)":[138],"feature-based":[145],"classifier":[146],"aggregates":[147],"signals":[149],"across":[150],"infer":[153],"attitudes\u2014even":[155],"when":[156],"itself":[160],"remains":[161],"unmentioned.":[162],"Experiments":[163],"social":[165],"media":[166],"financial":[168],"datasets":[170],"show":[171],"STS4MTS":[173],"outperforms":[174],"state-of-the-art":[175],"12\u201318%":[178],"detecting":[180],"indirect":[181],"These":[186],"findings":[187],"underscore":[188],"effectiveness":[190],"combining":[192],"decomposition":[194],"LLM-driven":[196],"analysis,":[197],"offering":[198],"scalable":[200],"approach":[201],"for":[202],"advancing":[203],"contentious":[209]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}
