Linguistic Analysis for Identifying Depression and Subsequent Suicidal Ideation on Weibo: Machine Learning Approaches

Wei Pan; Xianbin Wang; Wenwei Zhou; Bowen Hang; Liwen Guo

doi:10.3390/ijerph20032688

Linguistic Analysis for Identifying Depression and Subsequent Suicidal Ideation on Weibo: Machine Learning Approaches

Int J Environ Res Public Health. 2023 Feb 2;20(3):2688. doi: 10.3390/ijerph20032688.

Authors

Wei Pan^{1

2

3}, Xianbin Wang^{1

2

3}, Wenwei Zhou^{1

2

3}, Bowen Hang^{1

2

3}, Liwen Guo^{1

2

3}

Affiliations

¹ Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China.
² School of Psychology, Central China Normal University, Wuhan 430079, China.
³ Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan 430079, China.

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 R² = 0.64). For model performance: F-measure = 0.78, area under the curve (AUC) = 0.82. The independent samples' t-test showed that SI was significantly different between the depression (0.28 ± 0.5) and control groups (-0.29 ± 0.72) (t = 24.71, p < 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 R² was 0.42. For model performance, the correlation between the actual SI and predicted SI on the test set was significant (r = 0.65, p < 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.

Keywords: Weibo; depression; linguistic analysis; regression; suicidal ideation; topic modeling.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Depression / epidemiology
Depression / psychology
Humans
Linguistics
Machine Learning
Suicidal Ideation*
Suicide* / psychology

Grants and funding

This research was funded by the Fundamental Research Funds for the Central Universities (CCNU21XJ021), Knowledge Innovation Program of Wuhan-Shuguang Project (2022020801020288), and the Research Program Funds of the Collaborative Innovation Center of Assessment toward Basic Education Quality (2022-04-030-BZPK01).