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Data"],"published-print":{"date-parts":[[2023,12,31]]},"abstract":"<jats:p>Individuals 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\u2019 satisfaction, behaviors, cognition, and outcomes\u2014yet 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.<\/jats:p>","DOI":"10.1145\/3579096","type":"journal-article","created":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T12:17:46Z","timestamp":1673353066000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Multi-Stage Machine Learning Model for Hierarchical Tie Valence Prediction"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6030-1663","authenticated-orcid":false,"given":"Karandeep","family":"Singh","sequence":"first","affiliation":[{"name":"Data Science Group, Institute for Basic Science, Daejeon, South Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9756-0068","authenticated-orcid":false,"given":"Seungeon","family":"Lee","sequence":"additional","affiliation":[{"name":"Data Science Group, Institute for Basic Science, and School of Computing, KAIST, Daejeon, South Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9412-8421","authenticated-orcid":false,"given":"Giuseppe (Joe)","family":"Labianca","sequence":"additional","affiliation":[{"name":"Department of Management, UMass Amherst, and Department of Management, University of Exeter, Exeter, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1153-9442","authenticated-orcid":false,"given":"Jesse Michael","family":"Fagan","sequence":"additional","affiliation":[{"name":"Department of Management, University of Exeter, Exeter, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4085-9648","authenticated-orcid":false,"given":"Meeyoung","family":"Cha","sequence":"additional","affiliation":[{"name":"Data Science Group, Institute for Basic Science, and School of Computing, KAIST, Daejeon, South Korea"}]}],"member":"320","published-online":{"date-parts":[[2023,2,28]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.socnet.2021.04.003"},{"key":"e_1_3_3_3_2","first-page":"2591","volume-title":"Proceedings of the 23rd International Joint Conference on Artificial Intelligence","author":"Agrawal Priyanka","year":"2013","unstructured":"Priyanka Agrawal, Vikas K. 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