Institutional Repository, Institute of Psychology, Chinese Academy of Sciences
Uncovering the heterogeneous effects of depression on suicide risk conditioned by linguistic features: A double machine learning approach | |
Li, Sijia1![]() ![]() ![]() | |
第一作者 | Li, Sijia |
通讯作者 | Pan, Wei(panwei@ccnu.edu.cn) ; Yip, Paul Siu Fai(sfpyip@hku.hk) |
摘要 | Depression has been identified as a risk factor for suicide, yet limited evidence has elucidated the underlying pathways linking depression to subsequent suicide risk. Therefore, we aimed to examine the psychological mechanisms that connect depression to suicide risk via linguistic characteristics on Weibo. We sampled 487,251 posts from 3196 users who belong to the depression super-topic community (DSTC) on Sina Weibo as the depression group, and 357,939 posts from 5167 active users as the control group. We employed the double machine learning method (DML) to estimate the impact of depression on suicide risk, and interpreted the pathways from depression to suicide risk using SHapley Additive exPlanations (SHAP) values and tree interpreters. The results indicated an 18% higher likelihood of suicide risk in the depression group compared to people without depression. The SHAP values further revealed that Exclusive (M = 0.029) was the most critical linguistic feature. Meanwhile, the three-depth tree interpreter illustrated that the high suicide risk subgroup of the depression group (N = 1196, CATE = 0.32 ± 0.04, 95%CI [0.20, 0.43]) was predicted by higher usage of Exclusive (>0.59) and Health (>-0.10). DML revealed pathways linking depression to suicide risk. The visualized tree interpreter showed cognitive complexity and physical distress might be positively associated with suicide risk in depressed populations. These findings have invigorated further investigation to elucidate the relationship between depression and suicide risk. Understanding the underlying mechanisms serves as a basis for future research on suicide prevention and treatment for individuals with depression. |
关键词 | Depression Suicide risk Linguistic features Double machine learning Weibo |
2023 | |
语种 | 英语 |
DOI | 10.1016/j.chb.2023.108080 |
发表期刊 | 10.1016/j.chb.2023.108080
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ISSN | 0747-5632 |
卷号 | 152页码:10 |
收录类别 | EI |
资助项目 | Fundamental Research Funds for the Central Universities[CCNU21XJ021] ; Knowledge Innovation Program of Wuhan-Shuguang Project[2022020801020288] ; Research Program Funds of the Collaborative Innovation Center of Assessment toward Basic Education Quality[2022-04-030-BZPK01] ; Major Program of the National Social Science Foundation of China[22 ZD187] |
出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
WOS关键词 | LANGUAGE USE ; IDEATION ; METAANALYSIS ; INDIVIDUALS ; SYMPTOMS ; EPIDEMIOLOGY ; SAMPLE ; WORDS |
WOS研究方向 | Psychology |
WOS类目 | Psychology, Multidisciplinary ; Psychology, Experimental |
WOS记录号 | WOS:001137357200001 |
资助机构 | Fundamental Research Funds for the Central Universities ; Knowledge Innovation Program of Wuhan-Shuguang Project ; Research Program Funds of the Collaborative Innovation Center of Assessment toward Basic Education Quality ; Major Program of the National Social Science Foundation of China |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.psych.ac.cn/handle/311026/46599 |
专题 | 中国科学院心理研究所 |
作者单位 | 1.Department of Social Work and Social Administration, Faculty of Social Science, University of Hong Kong, Hong Kong 2.Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan, China 3.School of Psychology, Central China Normal University, Wuhan, China 4.Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan, China 5.Hong Kong Jockey Club Center for Suicide Research and Prevention, University of Hong Kong, Hong Kong 6.Institute of Psychology, Chinese Academy of Sciences, Beijing, China 7.Department of Psychology, University of Chinese Academy of Sciences, Beijing, China |
推荐引用方式 GB/T 7714 | Li, Sijia,Pan, Wei,Yip, Paul Siu Fai,et al. Uncovering the heterogeneous effects of depression on suicide risk conditioned by linguistic features: A double machine learning approach[J]. 10.1016/j.chb.2023.108080,2023,152:10. |
APA | Li, Sijia,Pan, Wei,Yip, Paul Siu Fai,Wang, Jing,Zhou, Wenwei,&Zhu, Tingshao.(2023).Uncovering the heterogeneous effects of depression on suicide risk conditioned by linguistic features: A double machine learning approach.10.1016/j.chb.2023.108080,152,10. |
MLA | Li, Sijia,et al."Uncovering the heterogeneous effects of depression on suicide risk conditioned by linguistic features: A double machine learning approach".10.1016/j.chb.2023.108080 152(2023):10. |
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