Abstract
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 (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 5,789 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.
Abstract (translated)
性少数群体（包括性别和性取向与异性恋及顺性人不同的个体）比他们的异性恋和顺性人同行经历更高的健康问题和精神障碍发生率，这主要是由于梅耶（2003年）模型中描述的“少数派压力”所致。本研究首次对基于变压器架构在在线话语中检测少数派压力的能力进行了全面评估。我们针对多个变压器模型，包括ELECTRA、BERT、RoBERTa和BART等，与传统机器学习基线以及图增强变体进行了基准测试，并进一步评估了零样本和少量样本学习范式以确定其在数据较少情况下的适用性。本研究使用两个最大的公开可用Reddit语料库进行实验，这些语料库包含12,645篇及5,789篇文章，并重复五次随机种子实验以确保结果的稳健性。 我们的结果显示，在仅基于变压器模型的情况下，整合图结构始终能够提高检测性能，且通过关系上下文监督微调的方法优于零样本和少量样本方法。理论分析表明，通过图增强建模社交联系和对话背景可以提升模型识别关键语言标志的能力，如身份隐藏、内化污名以及寻求支持的呼吁等，这说明图增强变压器为数字健康干预及公共卫生政策提供了最可靠的基石。
URL
https://arxiv.org/abs/2509.02908
