@Article{信息:doi 10.2196 / / jmir。7276，作者=“Cheng, Qijin and Li, Tim MH and Kwok, Chi-Leung and Zhu, Tingshao and Yip, Paul SF”，标题=“中国社交媒体的自杀风险和情绪困扰评估:文本挖掘和机器学习研究”，期刊=“J Med Internet Res”，年=“2017”，月=“7月”，日=“10”，卷=“19”，数=“7”，页=“e243”，关键词=“自杀;心理压力;社交媒体;中国人;自然语言;背景:早期识别和干预对于预防自杀是必要的。然而，高危人群往往既不寻求帮助，也不接受专业评估。在自然环境中自动评估其风险水平的工具可以增加早期干预的机会。目的:本研究旨在探讨计算机语言分析方法是否可用于评估中国社交媒体中的自杀风险和情绪困扰。 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. ", issn="1438-8871", doi="10.2196/jmir.7276", url="//www.mybigtv.com/2017/7/e243/", url="https://doi.org/10.2196/jmir.7276", url="http://www.ncbi.nlm.nih.gov/pubmed/28694239" }