Minority stress is the leading theoretical construct for understanding LGBTQ+ health disparities. As such, there is an urgent need to develop innovative policies and technologies to reduce minority stress. To spur technological innovation, we created the largest labeled datasets on minority stress using natural language from subreddits related to sexual and gender minority people. A team of mental health clinicians, LGBTQ+ health experts, and computer scientists developed two datasets: (1) the publicly available LGBTQ+ Minority Stress on Social Media (MiSSoM) dataset and (2) the advanced request-only version of the dataset, LGBTQ+ MiSSoM+. Both datasets have seven labels related to minority stress, including an overall composite label and six sublabels. LGBTQ+ MiSSoM (N = 27,709) includes both human- and machine-annotated la-bels and comes preprocessed with features (e.g., topic models, psycholinguistic attributes, sentiment, clinical keywords, word embeddings, n-grams, lexicons). LGBTQ+ MiSSoM+ includes all the characteristics of the open-access dataset, but also includes the original Reddit text and sentence-level labeling for a subset of posts (N = 5,772). Benchmark supervised machine learning analyses revealed that features of the LGBTQ+ MiSSoM datasets can predict overall minority stress quite well (F1 = 0.869). Benchmark performance metrics yielded in the prediction of the other labels, namely prejudiced events (F1 = 0.942), expected rejection (F1 = 0.964), internalized stigma (F1 = 0.952), identity concealment (F1 = 0.971), gender dysphoria (F1 = 0.947), and minority coping (F1 = 0.917), were excellent. Descriptive analyses, ethical considerations, limitations, and possible use cases are provided.

LGBTQ+ minority stress is a pervasive form of anti-LGBTQ+ adverse events and psychological strain that drives health inequities among LGBTQ+ people. Minority stress is also linguistically sophisticated (e.g., composed of cultural idioms, psycholinguistic permutations, and lexical density). Because minority stress is a linguistically sophisticated social determinant of health disparities, it is challenging to detect using natural language processing (NLP). Using 5,789 human-annotated Reddit posts from the LGBTQ+ Minority Stress on Social Media (MiSSoM+) Dataset, we investigated and compared the performance of four neural networks and two traditional machine learning architectures in modeling minority stress at both the factor (i.e., separate components of minority stress) and composite level. A novel hybrid model combining Bidirectional Encoder Representations from Transformers and convolutional neural network (BERT-CNN) improved the prediction of composite minority stress (F1 = 0.84). Our experiments on separate factors of minority stress are the first to demonstrate that hybrid neural network models can detect semantically complex expressions of prejudiced events (F1 = 0.87), expected rejection (F1 = 0.92), internalized stigma (F1 = 0.91), identity concealment (F1 = 0.92), and minority coping (F1 = 0.84). We also substantially improved the prediction of gender dysphoria (F1 = 0.94)—a conceptually new candidate component of minority stress. Big data analytics may not be a panacea for the problem of minority stress, but our work joins a growing literature base to show that deep learning models are remarkable in detecting linguistically sophisticated social determinants of health disparities in big data, thus providing evidence in support of the potential benefit from the innovative use of such technology in eliminating group-specific health inequities.