Detecting a Proxy for Potential Comorbid ADHD in People Reporting Anxiety Symptoms from Social Media Data
Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024) We present a novel task that can elucidate the connection between anxiety and ADHD; use Transformers to make progress toward solving a task that is not solvable by keyword-based classifiers; and discu...
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Zusammenfassung: | Proceedings of the 9th Workshop on Computational Linguistics and
Clinical Psychology (CLPsych 2024) We present a novel task that can elucidate the connection between anxiety and
ADHD; use Transformers to make progress toward solving a task that is not
solvable by keyword-based classifiers; and discuss a method for visualization
of our classifier illuminating the connection between anxiety and ADHD
presentations.
Up to approximately 50% of adults with ADHD may also have an anxiety disorder
and approximately 30\% of adults with anxiety may also have ADHD. Patients
presenting with anxiety may be treated for anxiety without ADHD ever being
considered, possibly affecting treatment. We show how data that bears on ADHD
that is comorbid with anxiety can be obtained from social media data, and show
that Transformers can be used to detect a proxy for possible comorbid ADHD in
people with anxiety symptoms.
We collected data from anxiety and ADHD online forums (subreddits). We
identified posters who first started posting in the Anxiety subreddit and later
started posting in the ADHD subreddit as well. We use this subset of the
posters as a proxy for people who presented with anxiety symptoms and then
became aware that they might have ADHD. We fine-tune a Transformer
architecture-based classifier to classify people who started posting in the
Anxiety subreddit and then started posting in the ADHD subreddit vs. people who
posted in the Anxiety subreddit without later posting in the ADHD subreddit. We
show that a Transformer architecture is capable of achieving reasonable results
(76% correct for RoBERTa vs. under 60% correct for the best keyword-based
model, both with 50% base rate). |
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DOI: | 10.48550/arxiv.2403.05561 |