- 성과
- 논문 (학술지) Multi-Stage Machine Learning Model for Hierarchical Tie Valence Prediction
| 등록번호 | - |
SCI 구분
※구분 : SCI(SCIE포함), 비SCI
|
SCI |
|---|---|---|---|
| 저자명 (주·공동저자) | -; | ||
| 논문구분 | 국외전문학술지 | 학술지명 | ACM Transactions on Knowledge Discovery in Data |
| ISSN | 1556-4681 | 학술지 출판일자 | 2023-01-10 |
| 학술지 볼륨번호 | 0(0) | 논문페이지 | 0 ~ 0 |
| 학술지 임팩트팩터 | 4.68 | 기여율 | 50 % |
| DOI | https://doi.org/10.1145/3579096 | ||
- 초록
- Individuals interacting in organizational settings involving varying levels of formal hierarchy naturally form a complex network of social ties having diferent tie valences (e.g., positive and negative connections). Social ties critically afect employees’ satisfaction, behaviors, cognition, and outcomes Ð 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 identiies 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 indings suggest that the presence of conversation data improves the tie valence classiication 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 diference 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 indings have broad implications for facilitating collaboration and managing conlict within organizational and other social networks.
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