# Advancing Minority Stress Detection with Transformers: Insights from the Social Media Datasets

arXiv: https://arxiv.org/abs/2509.02908 · HTML: https://mlsanity.com/paper/2509.02908/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 81 references that are in the map.
- First posted on arXiv 3 Sep 2025.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly](https://mlsanity.com/paper/1707.00600/) (2017)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [BertGCN: Transductive Text Classification by Combining GCN and BERT](https://mlsanity.com/paper/2105.05727/) (2021)
- [Detecting Harmful Online Conversational Content towards LGBTQIA+ Individuals](https://mlsanity.com/paper/2207.10032/) (2022)
- [Pruning Strategies for Backdoor Defense in LLMs](https://mlsanity.com/paper/2508.20032/) (2025)
- [Advancing Hate Speech Detection with Transformers: Insights from the MetaHate](https://mlsanity.com/paper/2508.04913/) (2025)
- [Predictive Insights into LGBTQ+ Minority Stress: A Transductive Exploration of Social Media Discourse](https://mlsanity.com/paper/2411.13534/) (2024)
- [Detecting LGBTQ+ Instances of Cyberbullying](https://mlsanity.com/paper/2409.12263/) (2024)

## Built on by (most cited first, 2 of 2)

- [TIMED: Adversarial and Autoregressive Refinement of Diffusion-Based Time Series Generation](https://mlsanity.com/paper/2509.19638/) (2025)
- [SolarGPT-QA: A Domain-Adaptive Large Language Model for Educational Question Answering in Space Weather and Heliophysics](https://mlsanity.com/paper/2601.12131/) (2026)

## Abstract (arXiv)

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.