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HelaDepDet: A Novel Multi-class Classification Model for Detecting the Severity of Human Depression

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Collaboration Technologies and Social Computing (CollabTech 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14199))

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Abstract

Depression-driven suicide is a serious social problem. Early identification of depression is vital for the well-being of society. Clinical diagnosis of depression takes a significant amount of time and requires highly skilled medical staff, which greatly limits its accessibility. Social media analysis for depression detection is therefore a rapidly growing research area. However, most of the available methods can only detect the presence or absence of depression, not the severity of depression. On the other hand, a few recently developed models for depression severity detection have not been validated on large datasets due to fundamental issues such as data sparsity. In this study, we proposed a novel method based on confidence vectors for detecting the severity of depression. We evaluated our method using a large dataset consisting of more than 40,000 annotated statements extracted from multiple social network services. To our knowledge, this is the largest and most well-balanced dataset for depression severity classification to date. Preliminary results showed that our models outperformed the existing state-of-the-art models by 5%, achieving a micro-averaged F1 score of 66% for human depression severity detection.

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Notes

  1. 1.

    https://github.com/CyraxSector/Depression_Severity_Levels_Dataset.

  2. 2.

    https://github.com/CyraxSector/HelaDepDet. The code will be released publicly in the near future for research purposes.

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Acknowledgements

We thank the anonymous reviewers for their valuable comments and suggestions.

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Authors and Affiliations

  1. Kyoto University of Advanced Science (KUAS), Kyoto, Japan

    Y. H. P. P. Priyadarshana, Zilu Liang & Ian Piumarta

Authors
  1. Y. H. P. P. Priyadarshana
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  2. Zilu Liang
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  3. Ian Piumarta
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Corresponding author

Correspondence to Y. H. P. P. Priyadarshana .

Editor information

Editors and Affiliations

  1. Ritsumeikan University, Shiga, Japan

    Hideyuki Takada

  2. Kyoto University of Advanced Science, Kyoto, Japan

    D. Moritz Marutschke

  3. Universidad de Los Andes, Las Condes, RM - Santiago, Chile

    Claudio Alvarez

  4. University of Tsukuba, Tsukuba, Japan

    Tomoo Inoue

  5. Ritsumeikan University, Ibaraki, Japan

    Yugo Hayashi

  6. Pompeu Fabra University, Barcelona, Spain

    Davinia Hernandez-Leo

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Priyadarshana, Y.H.P.P., Liang, Z., Piumarta, I. (2023). HelaDepDet: A Novel Multi-class Classification Model for Detecting the Severity of Human Depression. In: Takada, H., Marutschke, D.M., Alvarez, C., Inoue, T., Hayashi, Y., Hernandez-Leo, D. (eds) Collaboration Technologies and Social Computing. CollabTech 2023. Lecture Notes in Computer Science, vol 14199. Springer, Cham. https://doi.org/10.1007/978-3-031-42141-9_1

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  • DOI: https://doi.org/10.1007/978-3-031-42141-9_1

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