{"id":"https://openalex.org/W4321479998","doi":"https://doi.org/10.1145/3539597.3570401","title":"Learning Stance Embeddings from Signed Social Graphs","display_name":"Learning Stance Embeddings from Signed Social Graphs","publication_year":2023,"publication_date":"2023-02-22","ids":{"openalex":"https://openalex.org/W4321479998","doi":"https://doi.org/10.1145/3539597.3570401"},"language":"en","primary_location":{"id":"doi:10.1145/3539597.3570401","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539597.3570401","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","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/A5061597894","display_name":"John Pougu\u00e9-Biyong","orcid":"https://orcid.org/0000-0002-6582-193X"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"John Pougu\u00e9-Biyong","raw_affiliation_strings":["University of Oxford, Oxford, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-6582-193X","affiliations":[{"raw_affiliation_string":"University of Oxford, Oxford, United Kingdom","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023518844","display_name":"Akshay Gupta","orcid":"https://orcid.org/0000-0001-8782-5811"},"institutions":[{"id":"https://openalex.org/I4210111288","display_name":"Meta (United Kingdom)","ror":"https://ror.org/020ye1821","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210111288","https://openalex.org/I4210114444"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Akshay Gupta","raw_affiliation_strings":["Meta, London, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0001-8782-5811","affiliations":[{"raw_affiliation_string":"Meta, London, United Kingdom","institution_ids":["https://openalex.org/I4210111288"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039365507","display_name":"Aria Haghighi","orcid":"https://orcid.org/0000-0002-4997-0353"},"institutions":[{"id":"https://openalex.org/I113979032","display_name":"Twitter (United States)","ror":"https://ror.org/04wt43v05","country_code":"US","type":"company","lineage":["https://openalex.org/I113979032"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aria Haghighi","raw_affiliation_strings":["Twitter Cortex, Seattle, WA, USA"],"raw_orcid":"https://orcid.org/0000-0002-4997-0353","affiliations":[{"raw_affiliation_string":"Twitter Cortex, Seattle, WA, USA","institution_ids":["https://openalex.org/I113979032"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035119055","display_name":"Ahmed El-Kishky","orcid":"https://orcid.org/0000-0003-0121-7781"},"institutions":[{"id":"https://openalex.org/I113979032","display_name":"Twitter (United States)","ror":"https://ror.org/04wt43v05","country_code":"US","type":"company","lineage":["https://openalex.org/I113979032"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ahmed El-Kishky","raw_affiliation_strings":["Twitter Cortex, Seattle, WA, USA"],"raw_orcid":"https://orcid.org/0000-0003-0121-7781","affiliations":[{"raw_affiliation_string":"Twitter Cortex, Seattle, WA, USA","institution_ids":["https://openalex.org/I113979032"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.9579,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.88518421,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"177","last_page":"185"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998000264167786,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998000264167786,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9944000244140625,"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/computer-science","display_name":"Computer science","score":0.7351944446563721},{"id":"https://openalex.org/keywords/agreement","display_name":"Agreement","score":0.5524991750717163},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5140169858932495},{"id":"https://openalex.org/keywords/misinformation","display_name":"Misinformation","score":0.503175675868988},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4788927137851715},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.456512451171875},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4372771382331848},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.33284515142440796},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32824045419692993},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.28662729263305664}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7351944446563721},{"id":"https://openalex.org/C2776818064","wikidata":"https://www.wikidata.org/wiki/Q829903","display_name":"Agreement","level":2,"score":0.5524991750717163},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5140169858932495},{"id":"https://openalex.org/C2776990098","wikidata":"https://www.wikidata.org/wiki/Q13579947","display_name":"Misinformation","level":2,"score":0.503175675868988},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4788927137851715},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.456512451171875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4372771382331848},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.33284515142440796},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32824045419692993},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28662729263305664},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3539597.3570401","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539597.3570401","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.41999998688697815}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W108936587","https://openalex.org/W1888005072","https://openalex.org/W1968217253","https://openalex.org/W2112935688","https://openalex.org/W2142517301","https://openalex.org/W2154851992","https://openalex.org/W2187089797","https://openalex.org/W2402969480","https://openalex.org/W2585835859","https://openalex.org/W2595947069","https://openalex.org/W2622849676","https://openalex.org/W2743104969","https://openalex.org/W2783466287","https://openalex.org/W2788045146","https://openalex.org/W2896802857","https://openalex.org/W2962756421","https://openalex.org/W3012659934","https://openalex.org/W3087257704","https://openalex.org/W3099565317","https://openalex.org/W3104097132","https://openalex.org/W3126404394","https://openalex.org/W3175364065","https://openalex.org/W3175542874","https://openalex.org/W3202760958","https://openalex.org/W4290927951","https://openalex.org/W4290943549","https://openalex.org/W4291474301"],"related_works":["https://openalex.org/W3197131596","https://openalex.org/W4390616380","https://openalex.org/W4388666321","https://openalex.org/W4205914924","https://openalex.org/W4225301003","https://openalex.org/W4283459170","https://openalex.org/W4220949352","https://openalex.org/W4229014887","https://openalex.org/W4229067106","https://openalex.org/W4388798880"],"abstract_inverted_index":{"A":[0],"challenge":[1],"in":[2,26,83],"social":[3,27,85],"network":[4],"analysis,":[5],"is":[6],"understanding":[7],"the":[8,69,109,126],"position,":[9],"or":[10],"stance,":[11],"of":[12,18,42,111,128],"people":[13],"on":[14,48,114,162],"a":[15,40,112,173],"large":[16],"set":[17],"topics.":[19,44,60],"While":[20],"past":[21],"work":[22],"has":[23],"modeled":[24,36],"(dis)agreement":[25],"networks":[28],"using":[29,130,145],"signed":[30,84,134,154],"graphs,":[31],"these":[32],"approaches":[33],"have":[34,119],"not":[35,120],"agreement":[37,64],"patterns":[38],"across":[39],"range":[41],"correlated":[43],"For":[45],"instance,":[46],"disagreement":[47,53],"one":[49],"topic":[50,82,99,105],"may":[51],"make":[52],"(or":[54],"agreement)":[55],"more":[56],"likely":[57],"for":[58,78,91,116],"related":[59],"Recognizing":[61],"topics":[62,115],"influence":[63],"and":[65,81,98,164,169,175],"disagreement,":[66],"we":[67,118,138],"propose":[68],"Stance":[70],"Embeddings":[71],"Model":[72],"(SEM),":[73],"which":[74,117],"jointly":[75,95],"learns":[76],"embeddings":[77],"each":[79,92],"user":[80,97,113],"graphs":[86],"with":[87],"distinct":[88],"edge":[89],"types":[90],"topic.":[93],"By":[94],"learning":[96],"embeddings,":[100],"SEM":[101,129,171],"can":[102],"perform":[103],"cold-start":[104],"stance":[106,110],"detection,":[107],"predicting":[108],"observed":[121],"their":[122],"engagement.":[123],"We":[124],"demonstrate":[125],"effectiveness":[127],"two":[131],"large-scale":[132],"Twitter":[133],"graph":[135],"datasets":[136],"that":[137],"open-source.":[139],"One":[140],"dataset,":[141],"TwitterSG,":[142],"labels":[143],"(dis)agreements":[144],"engagements":[146],"between":[147],"users":[148],"via":[149],"tweets":[150],"to":[151],"derive":[152],"topic-informed,":[153],"edges.":[155],"The":[156],"other,":[157],"BirdwatchSG,":[158,170],"leverages":[159],"community":[160],"reports":[161],"misinformation":[163],"misleading":[165],"content.":[166],"On":[167],"TwitterSG":[168],"shows":[172],"39%":[174],"26%":[176],"error":[177],"reduction":[178],"respectively":[179],"against":[180],"strong":[181],"topic-agnostic":[182],"baselines.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
