A Multi-Scale Deep Learning Architecture for Psychological State Recognition and Early Risk Warning from Social Media Text.
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| Title: | A Multi-Scale Deep Learning Architecture for Psychological State Recognition and Early Risk Warning from Social Media Text. |
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| Authors: | CHEN, Yufei1 cyf2000404@163.com, CHEN, Kai2,3 c4kaichen@163.com |
| Source: | Technical Gazette / Tehnički Vjesnik. 2026, Vol. 33 Issue 3, p1148-1160. 13p. |
| Subjects: | Multiscale modeling, Mental health, Attention control, Twitter (Web resource), Natural language processing, Deep learning, Emotional state, Warnings |
| Abstract: | Social media has become an important channel for expressing emotional experiences and potential psychological distress, making automated psychological state recognition a key technical challenge for early risk warning systems. Psychological signals in text are distributed across multiple linguistic levels, ranging from character-level expressive variations to word-level semantics and sentence-level psychological structure, which limits the effectiveness of single-scale models. This paper proposes a multi-scale deep learning architecture for psychological state recognition from social media text. The approach integrates character-level and word-level representations, multi-scale convolutional modules for local semantic extraction, attention-based global semantic modeling, and cross-scale feature fusion. By jointly capturing fine-grained linguistic cues and global psychological context, the proposed model enhances the discriminative power of psychological representations. Experiments conducted on a multi-class mental health text dataset demonstrate that the proposed method consistently outperforms traditional machine learning models, conventional deep learning architectures, and attention-enhanced baselines in terms of accuracy, precision, recall, and F1-score. Furthermore, the model outputs are transformed into temporal risk signals, enabling the identification of weak, accumulating, and accelerating psychological risk patterns. The results indicate that multi-scale text modeling provides an effective technical solution for psychological state recognition and establishes a practical basis for the development of early psychological risk warning systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik 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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195131803 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Multi-Scale Deep Learning Architecture for Psychological State Recognition and Early Risk Warning from Social Media Text. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22CHEN%2C+Yufei%22">CHEN, Yufei</searchLink><relatesTo>1</relatesTo><i> cyf2000404@163.com</i><br /><searchLink fieldCode="AR" term="%22CHEN%2C+Kai%22">CHEN, Kai</searchLink><relatesTo>2,3</relatesTo><i> c4kaichen@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Technical+Gazette+%2F+Tehnički+Vjesnik%22">Technical Gazette / Tehnički Vjesnik</searchLink>. 2026, Vol. 33 Issue 3, p1148-1160. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Multiscale+modeling%22">Multiscale modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+health%22">Mental health</searchLink><br /><searchLink fieldCode="DE" term="%22Attention+control%22">Attention control</searchLink><br /><searchLink fieldCode="DE" term="%22Twitter+%28Web+resource%29%22">Twitter (Web resource)</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Emotional+state%22">Emotional state</searchLink><br /><searchLink fieldCode="DE" term="%22Warnings%22">Warnings</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Social media has become an important channel for expressing emotional experiences and potential psychological distress, making automated psychological state recognition a key technical challenge for early risk warning systems. Psychological signals in text are distributed across multiple linguistic levels, ranging from character-level expressive variations to word-level semantics and sentence-level psychological structure, which limits the effectiveness of single-scale models. This paper proposes a multi-scale deep learning architecture for psychological state recognition from social media text. The approach integrates character-level and word-level representations, multi-scale convolutional modules for local semantic extraction, attention-based global semantic modeling, and cross-scale feature fusion. By jointly capturing fine-grained linguistic cues and global psychological context, the proposed model enhances the discriminative power of psychological representations. Experiments conducted on a multi-class mental health text dataset demonstrate that the proposed method consistently outperforms traditional machine learning models, conventional deep learning architectures, and attention-enhanced baselines in terms of accuracy, precision, recall, and F1-score. Furthermore, the model outputs are transformed into temporal risk signals, enabling the identification of weak, accumulating, and accelerating psychological risk patterns. The results indicate that multi-scale text modeling provides an effective technical solution for psychological state recognition and establishes a practical basis for the development of early psychological risk warning systems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik 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: BibEntity: Identifiers: – Type: doi Value: 10.17559/TV-20251208003192 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1148 Subjects: – SubjectFull: Multiscale modeling Type: general – SubjectFull: Mental health Type: general – SubjectFull: Attention control Type: general – SubjectFull: Twitter (Web resource) Type: general – SubjectFull: Natural language processing Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Emotional state Type: general – SubjectFull: Warnings Type: general Titles: – TitleFull: A Multi-Scale Deep Learning Architecture for Psychological State Recognition and Early Risk Warning from Social Media Text. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: CHEN, Yufei – PersonEntity: Name: NameFull: CHEN, Kai IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13303651 Numbering: – Type: volume Value: 33 – Type: issue Value: 3 Titles: – TitleFull: Technical Gazette / Tehnički Vjesnik Type: main |
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