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Karandeep, Singh Brar
데이터 사이언스 그룹
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Multi-Stage Machine Learning Model for Hierarchical Tie Valence Prediction

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dc.contributor.authorKarandeep Singh-
dc.contributor.authorSeungeon Lee-
dc.contributor.authorLabianca, G.-
dc.contributor.authorFagan, J.M.-
dc.contributor.authorMeeyoung Cha-
dc.date.accessioned2023-07-27T22:00:29Z-
dc.date.available2023-07-27T22:00:29Z-
dc.date.created2023-05-11-
dc.date.issued2023-02-
dc.identifier.issn1556-4681-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/13653-
dc.description.abstractIndividuals interacting in organizational settings involving varying levels of formal hierarchy naturally form a complex network of social ties having different tie valences (e.g., positive and negative connections). Social ties critically affect employees' satisfaction, behaviors, cognition, and outcomes - yet identifying them solely through survey data is challenging because of the large size of some organizations or the often hidden nature of these ties and their valences. We present a novel deep learning model encompassing NLP and graph neural network techniques that identifies positive and negative ties in a hierarchical network. The proposed model uses human resource attributes as node information and web-logged work conversation data as link information. Our findings suggest that the presence of conversation data improves the tie valence classification by 8.91% compared to employing user attributes alone. This gain came from accurately distinguishing positive ties, particularly for male, non-minority, and older employee groups. We also show a substantial difference in conversation patterns for positive and negative ties with positive ties being associated with more messages exchanged on weekends, and lower use of words related to anger and sadness. These findings have broad implications for facilitating collaboration and managing conflict within organizational and other social networks. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.-
dc.language영어-
dc.publisherAssociation for Computing Machinery-
dc.titleMulti-Stage Machine Learning Model for Hierarchical Tie Valence Prediction-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000970563700008-
dc.identifier.scopusid2-s2.0-85154621950-
dc.identifier.rimsid80738-
dc.contributor.affiliatedAuthorKarandeep Singh-
dc.contributor.affiliatedAuthorSeungeon Lee-
dc.contributor.affiliatedAuthorMeeyoung Cha-
dc.identifier.doi10.1145/3579096-
dc.identifier.bibliographicCitationACM Transactions on Knowledge Discovery from Data, v.17, no.6-
dc.relation.isPartOfACM Transactions on Knowledge Discovery from Data-
dc.citation.titleACM Transactions on Knowledge Discovery from Data-
dc.citation.volume17-
dc.citation.number6-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryComputer Science, Software Engineering-
dc.subject.keywordPlusSTRUCTURAL BALANCE-
dc.subject.keywordPlusLINK PREDICTION-
dc.subject.keywordPlusENSEMBLE-
dc.subject.keywordAuthorgraph neural networks-
dc.subject.keywordAuthororganizational social network-
dc.subject.keywordAuthorsentiment embeddings-
dc.subject.keywordAuthorSigned link prediction-
dc.subject.keywordAuthortie-valence prediction-
Appears in Collections:
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > Data Science Group(데이터 사이언스 그룹) > 1. Journal Papers (저널논문)
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > 1. Journal Papers (저널논문)
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