Predictive Insights into LGBTQ+ Minority Stress: A Transductive Exploration of Social Media Discourse
Year: 2024
Type: conference-paper
Abstract: 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/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 humanannotated 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.
Authors: Santosh Chapagain, Yuxuan Zhao, Taylor K Rohleen, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi, Ryan E Flinn, Emily M Lund, Dannie Klooster, Jillian R Scheer, Cory J Cascalheira
Language: English
Topic: Mental Health via Writing
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
Awards: SHINE: Understanding the Relationships of Photospheric Vector Magnetic Field Parameters in Solar Flare Occurrences using Graph-based Machine Learning Models, CRII: OAC: Cyberinfrastructure for Machine Learning on Multivariate Time Series Data and Functional Networks, G3386659344, The UCLA HIV/AIDS, Substance Abuse, and Trauma Training Program, CAREER: End-to-End Active Region-based Heliospheric Forecasting System Using Multi-spacecraft Data and Machine Learning, Center to Improve System Performance of Substance Use Disorder Treatment, G8101356508, G9000293740, G9000306500, Collaborative Justice-Involved Research and Training Program on Substance Use and HIV at Brown University Health, Combining Physics and Machine Learning-based Models for Full-Energy-Range Solar Energetic Particles Events Prediction
OpenAlex ID: W4403724150