Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China.
School of Psychology, Central China Normal University, Wuhan 430079, China.
Int J Environ Res Public Health. 2023 Feb 2;20(3):2688. doi: 10.3390/ijerph20032688.
Depression is one of the most common mental illnesses but remains underdiagnosed. Suicide, as a core symptom of depression, urgently needs to be monitored at an early stage, i.e., the suicidal ideation (SI) stage. Depression and subsequent suicidal ideation should be supervised on social media. In this research, we investigated depression and concomitant suicidal ideation by identifying individuals' linguistic characteristics through machine learning approaches. On Weibo, we sampled 487,251 posts from 3196 users from the depression super topic community (DSTC) as the depression group and 357,939 posts from 5167 active users on Weibo as the control group. The results of the logistic regression model showed that the SCLIWC (simplified Chinese version of LIWC) features such as affection, positive emotion, negative emotion, sadness, health, and death significantly predicted depression (Nagelkerke's = 0.64). For model performance: = 0.78, area under the curve () = 0.82. The independent samples' -test showed that SI was significantly different between the depression (0.28 ± 0.5) and control groups (-0.29 ± 0.72) ( = 24.71, < 0.001). The results of the linear regression model showed that the SCLIWC features, such as social, family, affection, positive emotion, negative emotion, sadness, health, work, achieve, and death, significantly predicted suicidal ideation. The adjusted was 0.42. For model performance, the correlation between the actual SI and predicted SI on the test set was significant ( = 0.65, < 0.001). The topic modeling results were in accordance with the machine learning results. This study systematically investigated depression and subsequent SI-related linguistic characteristics based on a large-scale Weibo dataset. The findings suggest that analyzing the linguistic characteristics on online depression communities serves as an efficient approach to identify depression and subsequent suicidal ideation, assisting further prevention and intervention.
抑郁症是最常见的精神疾病之一，但仍未得到充分诊断。自杀作为抑郁症的核心症状，迫切需要早期监测，即自杀意念（SI）阶段。需要在社交媒体上监测抑郁症和随后的自杀意念。在这项研究中，我们通过机器学习方法识别个体的语言特征来研究抑郁症和伴随的自杀意念。我们从抑郁症超级话题社区（DSTC）中抽取了 3196 名用户的 487251 条微博作为抑郁症组，从微博上的 5167 名活跃用户中抽取了 357939 条微博作为对照组。逻辑回归模型的结果表明，SCLIWC（简化版的 LIWC）特征，如情感、积极情绪、消极情绪、悲伤、健康和死亡，显著预测了抑郁症（Nagelkerke's = 0.64）。对于模型性能： = 0.78，曲线下面积（AUC）= 0.82。独立样本的 t 检验显示，抑郁症组（0.28 ± 0.5）和对照组（-0.29 ± 0.72）的 SI 差异显著（ = 24.71， < 0.001）。线性回归模型的结果表明，SCLIWC 特征，如社交、家庭、情感、积极情绪、消极情绪、悲伤、健康、工作、成就和死亡，显著预测了自杀意念。调整后的 为 0.42。对于模型性能，测试集中实际 SI 和预测 SI 之间的相关性具有显著意义（ = 0.65， < 0.001）。主题建模结果与机器学习结果一致。本研究基于大规模微博数据集系统地研究了抑郁症和随后的 SI 相关语言特征。研究结果表明，分析在线抑郁症社区的语言特征是识别抑郁症和随后的自杀意念的有效方法，有助于进一步的预防和干预。
