ADHD-Lang: A Large-Scale Social Media Dataset for Verbal Behavior and Digital Phenotyping in Adult ADHD

Abstract

We introduce ADHD-Lang, a large-scale language resource derived from Reddit to advance computational phenotyping of adult ADHD. The corpus is constructed using a high-precision self-disclosure pattern to confirm ADHD diagnoses and a matched control cohort, comprising 12,070 ADHD users (317,073 posts; 2.83M sentences) and 12,070 controls (174,765 posts; 1.27M sentences). In releasing ADHD-Lang to the research community, we also provide the first comprehensive baseline results, systematically examining the accuracy–transparency trade-off across three model families: (1) interpretable shallow machine learning models trained on clinically meaningful, expert-engineered language biomarkers; (2) a deep BiLSTM network trained on the same feature representations to capture temporal dynamics across users’ posts; and (3) black-box transformer-based models (BERT, RoBERTa, MentalRoBERTa) leveraging contextual embeddings—non-interpretable, high-dimensional representations. ADHD-Lang is released as a standardized benchmark to promote reproducible research and accelerate progress toward digital verbal-behavior phenotyping for adult ADHD.

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