Postgraduate psychological stress detection from social media using BERT-Fused model Article Swipe
Muni Zhuang
,
Dongsheng Cheng
,
Xinzheng Lu
,
Xu Tan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0312264
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0312264
Postgraduate students face various academic, personal, and social stressors that increase their risk of anxiety, depression, and suicide. Identifying cost-effective methods of detecting and intervening before stress turns into severe problems is crucial. However, existing stress detection methods typically rely on psychological scales or devices, which can be complex and expensive. Therefore, we propose a BERT-fused model for rapidly and automatically detecting postgraduate students’ psychological stress via social media. First, we construct an improved BERT-LDA feature extraction algorithm to extract group stress features from large-scale and complex social media data. Then, we integrate the BiLSTM-CRF named entity recognition model to construct a multi-dimensional psychological stress profil e and analyze the fine-grained feature representation under the fusion of multi-dimensional features. Experimental results demonstrate that the proposed model outperforms traditional models such as BiLSTM, achieving an accuracy of 92.55%, a recall of 93.47%, and an F1-score of 92.18%, with F1-scores exceeding 89% for all three types of entities. This research provides both theoretical and practical foundations for universities or institutions to conduct fine-grained perception and intervention for postgraduate students’ psychological stress.
Related Topics
Concepts
Construct (python library)
Computer science
Anxiety
Social media
Stress (linguistics)
Recall
Artificial intelligence
Feature (linguistics)
Stressor
Psychology
Psychological stress
F1 score
Representation (politics)
Machine learning
Clinical psychology
Cognitive psychology
Psychiatry
World Wide Web
Politics
Programming language
Philosophy
Linguistics
Political science
Law
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0312264
- OA Status
- gold
- Cited By
- 1
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403938487
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403938487Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1371/journal.pone.0312264Digital Object Identifier
- Title
-
Postgraduate psychological stress detection from social media using BERT-Fused modelWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-31Full publication date if available
- Authors
-
Muni Zhuang, Dongsheng Cheng, Xinzheng Lu, Xu TanList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pone.0312264Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1371/journal.pone.0312264Direct OA link when available
- Concepts
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Construct (python library), Computer science, Anxiety, Social media, Stress (linguistics), Recall, Artificial intelligence, Feature (linguistics), Stressor, Psychology, Psychological stress, F1 score, Representation (politics), Machine learning, Clinical psychology, Cognitive psychology, Psychiatry, World Wide Web, Politics, Programming language, Philosophy, Linguistics, Political science, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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50Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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