Qijin AU - TY -的盟Cheng Li蒂姆•MH盟——郭Chi-Leung盟——朱Tingshao AU - Yip保罗科幻PY - 2017 DA - 2017/07/10 TI -评估自杀风险和情绪困扰中国社交媒体:文本挖掘和机器学习研究乔- J地中海互联网Res SP - e243六世- 19 - 7 KW自杀KW -心理压力KW -社会媒体KW -中国KW -自然语言KW -机器学习AB -背景:早期识别和干预对预防自杀至关重要。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。方法:对中国社交媒体(即微博)用户进行了一项基于网络的调查，以衡量他们的自杀风险因素，包括自杀概率、微博自杀传播(WSC)、抑郁、焦虑和压力水平。参与者在公共领域发布的微博帖子也在他们同意的情况下被下载。这些微博帖子被分析并归入简体中文语言查询和字数统计(SC-LIWC)类别。通过logistic回归分析SC-LIWC特征与5种自杀危险因素之间的关系。基于语言特征，应用支持向量机(SVM)模型对微博用户是否表现出上述5种风险因素中的任何一种进行自动分类。 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. SN - 1438-8871 UR - //www.mybigtv.com/2017/7/e243/ UR - https://doi.org/10.2196/jmir.7276 UR - http://www.ncbi.nlm.nih.gov/pubmed/28694239 DO - 10.2196/jmir.7276 ID - info:doi/10.2196/jmir.7276 ER -