Cory J. Cascalheira

Mehrab Beikzadeh, Chenglin Hong, Cory J Cascalheira et al.

Men who have sex with men (MSM) are at elevated risk for sexually transmitted infections and harmful drinking compared to heterosexual men. Text data collected from social media and dating applications may provide new opportunities for personalized public health interventions by enabling automatic identification of risk and protective behaviors. In this study, we evaluated whether text from social media and dating apps can be used to predict sexual risk behaviors, alcohol use, and pre-exposure prophylaxis (PrEP) uptake among MSM. With participant consent, we collected textual data and trained machine learning models using features derived from ChatGPT embeddings, BERT embeddings, LIWC, and a dictionary-based risk term approach. The models achieved strong performance in predicting monthly binge drinking and having more than five sexual partners, with F1 scores of 0.78, and moderate performance in predicting PrEP use and heavy drinking, with F1 scores of 0.64 and 0.63. These findings demonstrate that social media and dating app text data can provide valuable insights into risk and protective behaviors and highlight the potential of large language model-based methods to support scalable and personalized public health interventions for MSM.

S. Chapagain, Y. Zhao, T. K. Rohleen et al.

Individuals who identify as sexual and gender minorities, including lesbian, gay, bisexual, transgender, queer, and others (LGBTQ+) are more likely to experience poorer health than their heterosexual and cisgender counterparts. One primary source that drives these health disparities is minority stress (i.e., chronic and social stressors unique to LGBTQ+ communities' experiences adapting to the dominant culture). This stress is frequently expressed in LGBTQ+ users' posts on social media platforms. However, these expressions are not just straightforward manifestations of minority stress. They involve linguistic complexity (e.g., idiom or lexical diversity), rendering them challenging for many traditional natural language processing methods to detect. In this work, we designed a hybrid model using Graph Neural Networks (GNN) and Bidirectional Encoder Representations from Transformers (BERT), a pre-trained deep language model to improve the classification performance of minority stress detection. We experimented with our model on a benchmark social media dataset for minority stress detection (LGBTQ+ MiSSoM+). The dataset is comprised of 5,789 human-annotated Reddit posts from LGBTQ+ subreddits. Our approach enables the extraction of hidden linguistic nuances through pretraining on a vast amount of raw data, while also engaging in transductive learning to jointly develop representations for both labeled training data and unlabeled test data. The RoBERTa-GCN model achieved an accuracy of 0.86 and an F1 score of 0.86, surpassing the performance of other baseline models in predicting LGBTQ+ minority stress. Improved prediction of minority stress expressions on social media could lead to digital health interventions to improve the wellbeing of LGBTQ+ people-a community with high rates of stress-sensitive health problems.

Santosh Chapagain, Cory J Cascalheira, Shah Muhammad Hamdi et al.

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.