Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study

doi:10.2196/jmir.7276

	Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study
	Cheng, Qijin 1; Li, Tim M. H.2; Kwok, Chi-Leung 1; Zhu, Tingshao 3,4; Yip, Paul S. F.1
	2017-07-01
发表期刊	JOURNAL OF MEDICAL INTERNET RESEARCH
ISSN	1438-8871
卷号	19 期号:7 页码:10
摘要	Background: Early identification and intervention are imperative for suicide prevention. However, at-risk people often neither seek help nor take professional assessment. A tool to automatically assess their risk levels in natural settings can increase the opportunity for early intervention. Objective: The aim of this study was to explore whether computerized language analysis methods can be utilized to assess one's suicide risk and emotional distress in Chinese social media. Methods: A Web-based survey of Chinese social media (ie, Weibo) users was conducted to measure their suicide risk factors including suicide probability, Weibo suicide communication (WSC), depression, anxiety, and stress levels. Participants' Weibo posts published in the public domain were also downloaded with their consent. The Weibo posts were parsed and fitted into Simplified Chinese-Linguistic Inquiry and Word Count (SC-LIWC) categories. The associations between SC-LIWC features and the 5 suicide risk factors were examined by logistic regression. Furthermore, the support vector machine (SVM) model was applied based on the language features to automatically classify whether a Weibo user exhibited any of the 5 risk factors. Results: A total of 974 Weibo users participated in the survey. Those with high suicide probability were marked by a higher usage of pronoun (odds ratio, OR=1.18, P=.001), prepend words (OR=1.49, P=.02), multifunction words (OR=1.12, P=.04), a lower usage of verb (OR=0.78, P<.001), and a greater total word count (OR=1.007, P=.008). Second-person plural was positively associated with severe depression (OR=8.36, P=.01) and stress (OR=11, P=.005), whereas work-related words were negatively associated with WSC (OR=0.71, P=.008), severe depression (OR=0.56, P=.005), and anxiety (OR=0.77, P=.02). Inconsistently, third-person plural was found to be negatively associated with WSC (OR=0.02, P=.047) but positively with severe stress (OR=41.3, P=.04). Achievement-related words were positively associated with depression (OR=1.68, P=.003), whereas health-(OR=2.36, P=.004) and death-related (OR=2.60, P=.01) words positively associated with stress. The machine classifiers did not achieve satisfying performance in the full sample set but could classify high suicide probability (area under the curve, AUC=0.61, P=.04) and severe anxiety (AUC=0.75, P<.001) among those who have exhibited WSC. Conclusions: SC-LIWC is useful to examine language markers of suicide risk and emotional distress in Chinese social media and can identify characteristics different from previous findings in the English literature. Some findings are leading to new hypotheses for future verification. Machine classifiers based on SC-LIWC features are promising but still require further optimization for application in real life.
关键词	suicide psychological stress social media Chinese natural language machine learning
DOI	10.2196/jmir.7276
收录类别	SCI
语种	英语
资助项目	HKU[201601159010] ; HKU[17628916]
WOS研究方向	Health Care Sciences & Services ; Medical Informatics
WOS类目	Health Care Sciences & Services ; Medical Informatics
WOS记录号	WOS:000409228500001
出版者	JMIR PUBLICATIONS, INC
引用统计	被引频次：138[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/6646
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Cheng, Qijin
作者单位	1.Univ Hong Kong, HKJC Ctr Suicide Res & Prevent, 2-F Hong Kong Jockey Club Bldg Interdisciplinary, Hong Kong, Hong Kong, Peoples R China 2.Univ Hong Kong, LKS Fac Med, Dept Paediat & Adolescent Med, Hong Kong, Hong Kong, Peoples R China 3.Chinese Acad Sci, Inst Psychol, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Cheng, Qijin,Li, Tim M. H.,Kwok, Chi-Leung,et al. Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study[J]. JOURNAL OF MEDICAL INTERNET RESEARCH,2017,19(7):10.
APA	Cheng, Qijin,Li, Tim M. H.,Kwok, Chi-Leung,Zhu, Tingshao,&Yip, Paul S. F..(2017).Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study.JOURNAL OF MEDICAL INTERNET RESEARCH,19(7),10.
MLA	Cheng, Qijin,et al."Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study".JOURNAL OF MEDICAL INTERNET RESEARCH 19.7(2017):10.