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home / papers / 2026 / ADHD-Lang: A Large-Scale Social Media Dataset for Verbal Behavior and Digital Phenotyping in Adult ADHD

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

Summary

Adult ADHD is often missed or recognized late. Standard assessment depends on interviews and retrospective self-report scales, and these were originally designed around childhood symptoms. Social media language has been widely studied for depression, anxiety and suicidality, but adult ADHD has had no comparable benchmark. The authors fill this gap with ADHD-Lang, a Reddit corpus. Users who explicitly mention an official diagnosis are identified with a strict self-disclosure pattern and paired with an equally sized control group, using procedures adapted from earlier resources such as SMHD.

They then report baselines across the accuracy–transparency spectrum:

  • shallow classifiers trained on expert-designed linguistic features;
  • a BiLSTM that reads the same features as sequences of posts;
  • transformer models (BERT, RoBERTa, MentalRoBERTa).

A feature-importance analysis of the best shallow model shows which cues distinguish the groups. It covers lexical signals and also content-independent measures of sophistication, structural complexity and precision. The specific scores are in a section missing from the supplied text. The resource gives researchers a reproducible testbed for verbal-behavior markers of adult ADHD.

Key points

  • Corpus size: 12,070 ADHD users (317,073 posts; 2.83M sentences) and 12,070 matched controls (174,765 posts; 1.27M sentences).
  • Model families: feature-based shallow ML, a feature-based BiLSTM, and BERT / RoBERTa / MentalRoBERTa. Feature importance is measured with Gini impurity in a Random Forest.
  • Access: non-commercial research use only, under a data use agreement modeled on SMHD. Only de-identified feature representations, data splits and preprocessing code are shared, with no raw Reddit text.
  • Limitations: possible bias from relying on public self-disclosure, a cross-sectional rather than longitudinal design, and baselines that are not tuned systems.
  • Intended use: research only, explicitly not individual diagnosis or clinical decisions.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/3uw9c6ux3f7j

@inproceedings{wiechmann2026adhd,
  title = {{ADHD-Lang: A Large-Scale Social Media Dataset for Verbal Behavior and Digital Phenotyping in Adult ADHD}},
  author = {Wiechmann, Daniel and Kerz, Elma and Kempa, Edward and Qiao, Yu},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/3uw9c6ux3f7j},
  url = {https://doi.org/10.63317/3uw9c6ux3f7j}
}

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