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This is a Literature Review study.

September 10, 2025

Applications of Transformer-Based Language Models for Depression Detection: A Scoping Review

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Authors

HMHann-Shin MaoKeldysh Institute of Applied Mathematics QHQing HanZhejiang Chinese Medical University

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Implication

Scoping review examines the effectiveness of transformer-based language models in detecting depression from text data, suggesting implications for psychiatric practice.

Key Points

Transformer-based language models effectively detect depression in text data, which shows their potential in mental health applications.
Predominance of models like BERT indicates significant advancements in understanding semantic nuances for depression detection.
Analysis of social media texts highlights Twitter as a key data source for exploring emotional and contextual cues related to depression.
Emerging models such as MentalBERT demonstrate the capacity to enhance detection through adaptation to context-specific linguistic features.

Cite This Study

Mao et al. (2025) studied this question.

synapsesocial.com/papers/68c1ac0954b1d3bfb60e48c5 https://doi.org/10.62836/jissh.v2i1.437

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Implication

Scoping review examines the effectiveness of transformer-based language models in detecting depression from text data, suggesting implications for psychiatric practice.

Applications of Transformer-Based Language Models for Depression Detection: A Scoping Review

Discussion

Member takes

Implication

Key Points

Cite This Study

Applications of Transformer-Based Language Models for Depression Detection: A Scoping Review

Discussion

Member takes

Implication

Key Points

Cite This Study

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