IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i3p2688-d1055661.html
   My bibliography  Save this article

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

Author

Listed:
  • Wei Pan

    (Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China
    School of Psychology, Central China Normal University, Wuhan 430079, China
    Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan 430079, China
    These authors contributed equally to this work.)

  • Xianbin Wang

    (Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China
    School of Psychology, Central China Normal University, Wuhan 430079, China
    Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan 430079, China
    These authors contributed equally to this work.)

  • Wenwei Zhou

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

  • Bowen Hang

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

  • Liwen Guo

    (Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan 430079, China
    School of Psychology, Central China Normal University, Wuhan 430079, China
    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 R 2 = 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 R 2 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.

Suggested Citation

  • Wei Pan & Xianbin Wang & Wenwei Zhou & Bowen Hang & Liwen Guo, 2023. "Linguistic Analysis for Identifying Depression and Subsequent Suicidal Ideation on Weibo: Machine Learning Approaches," IJERPH, MDPI, vol. 20(3), pages 1-12, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:3:p:2688-:d:1055661
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/3/2688/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/3/2688/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yun Gu & Deyuan Chen & Xiaoqian Liu, 2022. "Suicide Possibility Scale Detection via Sina Weibo Analytics: Preliminary Results," IJERPH, MDPI, vol. 20(1), pages 1-11, December.
    2. Jingfang Liu & Mengshi Shi & Huihong Jiang, 2022. "Detecting Suicidal Ideation in Social Media: An Ensemble Method Based on Feature Fusion," IJERPH, MDPI, vol. 19(13), pages 1-13, July.
    3. Feng Huang & Sijia Li & Dongqi Li & Meizi Yang & Huimin Ding & Yazheng Di & Tingshao Zhu, 2022. "The Impact of Mortality Salience, Negative Emotions and Cultural Values on Suicidal Ideation in COVID-19: A Conditional Process Model," IJERPH, MDPI, vol. 19(15), pages 1-15, July.
    4. Theyazn H. H. Aldhyani & Saleh Nagi Alsubari & Ali Saleh Alshebami & Hasan Alkahtani & Zeyad A. T. Ahmed, 2022. "Detecting and Analyzing Suicidal Ideation on Social Media Using Deep Learning and Machine Learning Models," IJERPH, MDPI, vol. 19(19), pages 1-16, October.
    5. G. N. Wilkinson & C. E. Rogers, 1973. "Symbolic Description of Factorial Models for Analysis of Variance," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 22(3), pages 392-399, November.
    6. Shaoxiong Ji & Celina Ping Yu & Sai-fu Fung & Shirui Pan & Guodong Long, 2018. "Supervised Learning for Suicidal Ideation Detection in Online User Content," Complexity, Hindawi, vol. 2018, pages 1-10, September.
    7. Sijia Li & Yilin Wang & Jia Xue & Nan Zhao & Tingshao Zhu, 2020. "The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users," IJERPH, MDPI, vol. 17(6), pages 1-9, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yun Gu & Deyuan Chen & Xiaoqian Liu, 2022. "Suicide Possibility Scale Detection via Sina Weibo Analytics: Preliminary Results," IJERPH, MDPI, vol. 20(1), pages 1-11, December.
    2. Israel Escudero-Castillo & Fco. Javier Mato-Díaz & Ana Rodriguez-Alvarez, 2021. "Furloughs, Teleworking and Other Work Situations during the COVID-19 Lockdown: Impact on Mental Well-Being," IJERPH, MDPI, vol. 18(6), pages 1-16, March.
    3. S. Brent Jackson & Kathryn T. Stevenson & Lincoln R. Larson & M. Nils Peterson & Erin Seekamp, 2021. "Outdoor Activity Participation Improves Adolescents’ Mental Health and Well-Being during the COVID-19 Pandemic," IJERPH, MDPI, vol. 18(5), pages 1-18, March.
    4. Massimiliano Scopelliti & Maria Giuseppina Pacilli & Antonio Aquino, 2021. "TV News and COVID-19: Media Influence on Healthy Behavior in Public Spaces," IJERPH, MDPI, vol. 18(4), pages 1-15, February.
    5. Yuan Zheng & Jingyi Zhou & Xianglong Zeng & Mingyan Jiang & Tian P. S. Oei, 2022. "A New Second-Generation Mindfulness-Based Intervention Focusing on Well-Being: A Randomized Control Trial of Mindfulness-Based Positive Psychology," Journal of Happiness Studies, Springer, vol. 23(6), pages 2703-2724, August.
    6. Clemens Koestner & Viktoria Eggert & Theresa Dicks & Kristin Kalo & Carolina Zähme & Pavel Dietz & Stephan Letzel & Till Beutel, 2022. "Psychological Burdens among Teachers in Germany during the SARS-CoV-2 Pandemic—Subgroup Analysis from a Nationwide Cross-Sectional Online Survey," IJERPH, MDPI, vol. 19(15), pages 1-16, August.
    7. Durmuş Burak, 2023. "The Effect of Risk and Protective Factors on Primary School Students’ COVID-19 Anxiety: Back to School After the Pandemic," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 16(1), pages 29-51, February.
    8. Maraseni, Tek & Poudyal, Bishnu Hari & Aryal, Kishor & Laudari, Hari Krishna, 2022. "Impact of COVID-19 in the forestry sector: A case of lowland region of Nepal," Land Use Policy, Elsevier, vol. 120(C).
    9. Ilse Adriana Gutiérrez-Pérez & Pedro Delgado-Floody & Daniel Jerez-Mayorga & Diego Soto-García & Felipe Caamaño-Navarrete & Isela Parra-Rojas & Nacim Molina-Gutiérrez & Iris Paola Guzmán-Guzmán, 2021. "Lifestyle and Sociodemographic Parameters Associated with Mental and Physical Health during COVID-19 Confinement in Three Ibero-American Countries. A Cross-Sectional Pilot Study," IJERPH, MDPI, vol. 18(10), pages 1-13, May.
    10. Payne, Roger W., 1998. "Design keys, pseudo-factors and general balance," Computational Statistics & Data Analysis, Elsevier, vol. 29(2), pages 217-229, December.
    11. Christian M. Hafner, 2020. "The Spread of the Covid-19 Pandemic in Time and Space," IJERPH, MDPI, vol. 17(11), pages 1-13, May.
    12. Siqi Lai & Brian Deal, 2022. "Parks, Green Space, and Happiness: A Spatially Specific Sentiment Analysis Using Microblogs in Shanghai, China," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    13. Jadsada Kunno & Busaba Supawattanabodee & Chavanant Sumanasrethakul & Chuthamat Kaewchandee & Wachiraporn Wanichnopparat & Krit Prasittichok, 2022. "The Relationship between Attitudes and Satisfaction Concerning the COVID-19 Vaccine and Vaccine Boosters in Urban Bangkok, Thailand: A Cross-Sectional Study," IJERPH, MDPI, vol. 19(9), pages 1-11, April.
    14. Mateusz Ciski & Krzysztof Rząsa, 2023. "Multiscale Geographically Weighted Regression in the Investigation of Local COVID-19 Anomalies Based on Population Age Structure in Poland," IJERPH, MDPI, vol. 20(10), pages 1-23, May.
    15. Yang Yang & Keqiao Liu & Siqi Li & Man Shu, 2020. "Social Media Activities, Emotion Regulation Strategies, and Their Interactions on People’s Mental Health in COVID-19 Pandemic," IJERPH, MDPI, vol. 17(23), pages 1-16, December.
    16. Alessandro Germani & Livia Buratta & Elisa Delvecchio & Claudia Mazzeschi, 2020. "Emerging Adults and COVID-19: The Role of Individualism-Collectivism on Perceived Risks and Psychological Maladjustment," IJERPH, MDPI, vol. 17(10), pages 1-15, May.
    17. Jacqueline-Nathalie Harba & Gabriela Tigu & Adriana AnaMaria Davidescu, 2021. "Exploring Consumer Emotions in Pre-Pandemic and Pandemic Times. A Sentiment Analysis of Perceptions in the Fine-Dining Restaurant Industry in Bucharest, Romania," IJERPH, MDPI, vol. 18(24), pages 1-24, December.
    18. Bent Nielsen, 2014. "Deviance analysis of age-period-cohort models," Economics Papers 2014-W03, Economics Group, Nuffield College, University of Oxford.
    19. Juwon Hwang & Porismita Borah & Dhavan Shah & Markus Brauer, 2021. "The Relationship among COVID-19 Information Seeking, News Media Use, and Emotional Distress at the Onset of the Pandemic," IJERPH, MDPI, vol. 18(24), pages 1-13, December.
    20. Juan Gómez-Salgado & Montserrat Andrés-Villas & Sara Domínguez-Salas & Diego Díaz-Milanés & Carlos Ruiz-Frutos, 2020. "Related Health Factors of Psychological Distress During the COVID-19 Pandemic in Spain," IJERPH, MDPI, vol. 17(11), pages 1-16, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:20:y:2023:i:3:p:2688-:d:1055661. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.