ArticleInternational journal of environmental research and public health2023
Linguistic Analysis for Identifying Depression and Subsequent Suicidal Ideation on Weibo: Machine Learning Approaches.
Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 9 citations in OpenAlex.
- Multidimensional analysis of childbirth experiences on Chinese social media: an integrated approach using LDA, sentiment analysis and deep learning.Frontiers in public health · 2026Article
- A Digital Approach for Addressing Suicidal Ideation and Behaviors in Youth Mental Health Services: Observational Study.Journal of medical Internet research · 2024Observational
- Knowledge, attitudes, and practices among medical students toward depression management: a cross-sectional study in China.Frontiers in public health · 2024Article
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Authors and funding
5 authors at 1 institution in 1 country.
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Abstract
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
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