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Dep-capsule: capsule network for depression detection of Chinese microblog users.

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Title: Dep-capsule: capsule network for depression detection of Chinese microblog users.
Authors: Li, Ran1,2 (AUTHOR) liran@gzhu.edu.cn, Wang, Simin2 (AUTHOR) wangsimin056@e.gzhu.edu.cn, Sun, Zhe2 (AUTHOR) sunzhe@gzhu.edu.cn, Zhang, Aohai2,3 (AUTHOR) zhangaohai@e.gzhu.edu.cn, Luo, Yuxuan2 (AUTHOR) luo_yuxuan@e.gzhu.edu.cn, Peng, Xingyi2 (AUTHOR) pengxingyi@e.gzhu.edu.cn, Li, Chao2 (AUTHOR) lichao@gzhu.edu.cn
Source: Kybernetes. 2026, Vol. 55 Issue 2, p922-943. 22p.
Subjects: Capsule neural networks, Microblogs, Emotional state, Social networks, Sentiment analysis, Psychometrics, Facial expression & emotions (Psychology)
Abstract: Purpose: Depression has become one of the most serious and prevalent mental health problems worldwide. The rise and popularity of social networks such as microblogs provides a wealth of psychological data for early depression detection. Language use patterns reflect emotional states and psychological traits. Differences in language use between depressed and general users may help predict and diagnose early depression. Existing work focuses on depression detection using users' social textual emotion expressions, with less psychology-related knowledge. Design/methodology/approach: In this paper, we propose an RNN-capsule-based depression detection method for microblog users that improves depression detection accuracy in social texts by combining textual emotional information with knowledge related to depression pathology. Specifically, we design a multi-classification RNN capsule that enhances emotion expression features in utterances and improves classification performance of depression-related emotional features. Based on user emotion annotations over time, we use integrated learning to detect depression in a user's social text by combining the analysis results with components such as emotion change vector, emotion causality analysis, depression lexicon and the presence of surprising emotions. Findings: In our experiments, we test the accuracy of RNN capsules for emotion classification tasks and then validate the effectiveness of different depression detection components. Finally, we achieved 83% depression detection accuracy on real datasets. Originality/value: The paper overcomes the limitations of social text-based depression detection by incorporating more psychological background knowledge to enhance the early detection success rate of depression. [ABSTRACT FROM AUTHOR]
Copyright of Kybernetes is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 191147863
AccessLevel: 6
PubType: Periodical
PubTypeId: serialPeriodical
PreciseRelevancyScore: 0
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Dep-capsule: capsule network for depression detection of Chinese microblog users.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Li%2C+Ran%22">Li, Ran</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> liran@gzhu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Simin%22">Wang, Simin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> wangsimin056@e.gzhu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Sun%2C+Zhe%22">Sun, Zhe</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> sunzhe@gzhu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Aohai%22">Zhang, Aohai</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> zhangaohai@e.gzhu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Luo%2C+Yuxuan%22">Luo, Yuxuan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> luo_yuxuan@e.gzhu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Peng%2C+Xingyi%22">Peng, Xingyi</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> pengxingyi@e.gzhu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Chao%22">Li, Chao</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> lichao@gzhu.edu.cn</i>
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  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Kybernetes%22">Kybernetes</searchLink>. 2026, Vol. 55 Issue 2, p922-943. 22p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Capsule+neural+networks%22">Capsule neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Microblogs%22">Microblogs</searchLink><br /><searchLink fieldCode="DE" term="%22Emotional+state%22">Emotional state</searchLink><br /><searchLink fieldCode="DE" term="%22Social+networks%22">Social networks</searchLink><br /><searchLink fieldCode="DE" term="%22Sentiment+analysis%22">Sentiment analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Psychometrics%22">Psychometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Facial+expression+%26+emotions+%28Psychology%29%22">Facial expression & emotions (Psychology)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose: Depression has become one of the most serious and prevalent mental health problems worldwide. The rise and popularity of social networks such as microblogs provides a wealth of psychological data for early depression detection. Language use patterns reflect emotional states and psychological traits. Differences in language use between depressed and general users may help predict and diagnose early depression. Existing work focuses on depression detection using users' social textual emotion expressions, with less psychology-related knowledge. Design/methodology/approach: In this paper, we propose an RNN-capsule-based depression detection method for microblog users that improves depression detection accuracy in social texts by combining textual emotional information with knowledge related to depression pathology. Specifically, we design a multi-classification RNN capsule that enhances emotion expression features in utterances and improves classification performance of depression-related emotional features. Based on user emotion annotations over time, we use integrated learning to detect depression in a user's social text by combining the analysis results with components such as emotion change vector, emotion causality analysis, depression lexicon and the presence of surprising emotions. Findings: In our experiments, we test the accuracy of RNN capsules for emotion classification tasks and then validate the effectiveness of different depression detection components. Finally, we achieved 83% depression detection accuracy on real datasets. Originality/value: The paper overcomes the limitations of social text-based depression detection by incorporating more psychological background knowledge to enhance the early detection success rate of depression. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Kybernetes is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 22
        StartPage: 922
    Subjects:
      – SubjectFull: Capsule neural networks
        Type: general
      – SubjectFull: Microblogs
        Type: general
      – SubjectFull: Emotional state
        Type: general
      – SubjectFull: Social networks
        Type: general
      – SubjectFull: Sentiment analysis
        Type: general
      – SubjectFull: Psychometrics
        Type: general
      – SubjectFull: Facial expression & emotions (Psychology)
        Type: general
    Titles:
      – TitleFull: Dep-capsule: capsule network for depression detection of Chinese microblog users.
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            NameFull: Li, Ran
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            NameFull: Wang, Simin
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            NameFull: Sun, Zhe
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            NameFull: Zhang, Aohai
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            NameFull: Luo, Yuxuan
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            NameFull: Peng, Xingyi
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            NameFull: Li, Chao
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            – D: 15
              M: 01
              Text: 2026
              Type: published
              Y: 2026
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              Value: 55
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