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Developing a machine learning-based instrument for subjective well-being assessment on Weibo and its psychological significance: An evaluative and interpretive research
Han, Nuo1,2; Wen, Yeye3; Wang, Bowen4; Huang, Feng5,6; Liu, Xiaoqian5; Li, Linyan2,7; Zhu, Tingshao5,6
通讯作者Li, Linyan(linyanli@cityu.edu.hk) ; Zhu, Tingshao(tszhu@psych.ac.cn)
摘要Demystifying machine learning (ML) approaches through the synergy of psychology and artificial intelligence can achieve a balance between predictive and explanatory power in model development while enhancing rigor in validation and reporting standards. Accordingly, this study aimed to bridge this research gap by developing a subjective well-being (SWB) prediction model on Weibo, serving as a psychological assessment instrument and explaining the model construction based on psychological knowledge. The model establishment involved the collection of SWB scores and posts from 1,427 valid Weibo users. Multiple machine learning algorithms were employed to train the model and fine-tune its parameters. The optimal model was selected by comparing its criterion validity and split-half reliability performance. Furthermore, SHAP values were calculated to rank the importance of features, which were then used for model interpretation. The criterion validity for the three dimensions of SWB ranged from 0.50 to 0.52 (P < 0.001), and the split-half reliability ranged from 0.94 to 0.96 (P < 0.001). The identified relevant features were related to four main aspects: cultural values, emotions, morality, and time and space. This study expands the application scope of SWB-related psychological theories from a data-driven perspective and provides a theoretical reference for further well-being prediction.
关键词domain knowledge life satisfaction machine learning social media subjective well-being Weibo
2024-08-21
语种英语
DOI10.1111/aphw.12590
发表期刊APPLIED PSYCHOLOGY-HEALTH AND WELL BEING
ISSN1758-0846
页码20
收录类别SCI
出版者WILEY
WOS关键词AFFECT SCHEDULE PANAS ; SOCIAL MEDIA ; PERSONALITY ; FACEBOOK ; TWITTER ; HAPPINESS ; VALIDITY ; REFLECT ; CULTURE ; EVENTS
WOS研究方向Psychology
WOS类目Psychology, Applied
WOS记录号WOS:001295402200001
引用统计
文献类型期刊论文
条目标识符https://ir.psych.ac.cn/handle/311026/49105
通讯作者Li, Linyan; Zhu, Tingshao
作者单位1.Beijing Normal Univ, Fac Arts & Sci, Dept Psychol, Zhuhai, Peoples R China
2.City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China
3.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing, Peoples R China
4.Helmholtz Ctr Potsdam, GFZ German Res Ctr Geosci, Potsdam, Germany
5.Chinese Acad Sci, Inst Psychol, CAS Key Lab Behav Sci, Beijing, Peoples R China
6.Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China
7.City Univ Hong Kong, Jockey Club Coll Vet Med & Life Sci, Dept Infect Dis & Publ Hlth, Hong Kong, Peoples R China
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GB/T 7714
Han, Nuo,Wen, Yeye,Wang, Bowen,et al. Developing a machine learning-based instrument for subjective well-being assessment on Weibo and its psychological significance: An evaluative and interpretive research[J]. APPLIED PSYCHOLOGY-HEALTH AND WELL BEING,2024:20.
APA Han, Nuo.,Wen, Yeye.,Wang, Bowen.,Huang, Feng.,Liu, Xiaoqian.,...&Zhu, Tingshao.(2024).Developing a machine learning-based instrument for subjective well-being assessment on Weibo and its psychological significance: An evaluative and interpretive research.APPLIED PSYCHOLOGY-HEALTH AND WELL BEING,20.
MLA Han, Nuo,et al."Developing a machine learning-based instrument for subjective well-being assessment on Weibo and its psychological significance: An evaluative and interpretive research".APPLIED PSYCHOLOGY-HEALTH AND WELL BEING (2024):20.
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