A Multi-Scale Deep Learning Architecture for Psychological State Recognition and Early Risk Warning from Social Media Text.

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Bibliographic Details
Title:	A Multi-Scale Deep Learning Architecture for Psychological State Recognition and Early Risk Warning from Social Media Text.
Authors:	CHEN, Yufei¹ cyf2000404@163.com, CHEN, Kai^2,3 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]
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Database:	Engineering Source

Description
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]
ISSN:	13303651
DOI:	10.17559/TV-20251208003192