Original scientific paper

https://doi.org/10.17559/TV-20251208003192

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

Yufei Chen ; Changzhou Saixun Network Technology Co., Ltd., Jiangsu, China
Kai Chen ; 1) Changzhou No.2 People's Hospital, Jiangsu, China 2) The Third Affiliated Hospital of Nanjing Medical University, Changzhou 213003, Jiangsu, China *

* Corresponding author.

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.

Keywords

computational mental health; early risk warning; multi-scale modelling; psychological state recognition; social media analysis

Hrčak ID:

346727

URI

https://hrcak.srce.hr/346727

Publication date:

30.4.2026.

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