Advancing minority stress detection with transformers

Insights from the social media datasets

Bibliographic Data

ID16859487
AuthorsSantosh Chapagain (0009-0008-1590-5917, Utah State University, corresponding author), Cory J Cascalheira (0000-0001-5780-3101, VA Puget Sound Health Care System), Shah Muhammad Hamdi (0000-0002-9303-7835, Utah State University), Soukaina Filali Boubrahimi (0000-0001-5693-6383, Utah State University), Jillian R Scheer (0000-0002-7311-5904, University of Rhode Island)
Year2025
Volume15
Issue1
Publication date2025-09-03
Open AccessYes
TypeARTICLE
VenueSocial Network Analysis and Mining (JOURNAL)
Journal identifiersISSN: 1869-5450 • E-ISSN: 1869-5469
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s13278-025-01521-z
PMID42110334
OpenAlexW4413947764
LanguageEN
References cited36

Individuals from sexual and gender minority groups experience disproportionately high rates of poor health outcomes and mental disorders compared to their heterosexual and cisgender counterparts, largely as a consequence of minority stress as described by Meyer’s (Psychol Bull 129(5):674, 2003) model. This study presents the first comprehensive evaluation of transformer-based architectures for detecting minority stress in online discourse. We benchmark multiple transformer models including ELECTRA, BERT, RoBERTa, and BART against traditional machine learning baselines and graph-augmented variants. We further assess zero-shot and few-shot learning paradigms to assess their applicability on underrepresented datasets. Experiments are conducted on the two largest publicly available Reddit corpora for minority stress detection, comprising 12,645 and 5789 posts, and are repeated over five random seeds to ensure robustness. Our results demonstrate that integrating graph structure consistently improves detection performance across transformer-only models and that supervised fine-tuning with relational context outperforms zero and few-shot approaches. Theoretical analysis reveals that modeling social connectivity and conversational context via graph augmentation sharpens the models’ ability to identify key linguistic markers such as identity concealment, internalized stigma, and calls for support, suggesting that graph-enhanced transformers offer the most reliable foundation for digital health interventions and public health policy

Biology · Computational biology · Electrical engineering · Social media · Transformer · World Wide Web · Computer Science · Engineering · Hate Speech and Cyberbullying Detection · Mental Health via Writing · Sentiment Analysis and Opinion Mining

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