Transparent but Powerful: Explainability, Accuracy, and Generalizability in ADHD Detection from Social Media Data

TL;DR

This study explores AI-based methods for detecting ADHD from social media text, balancing explainability and accuracy, and testing model generalizability across Reddit and Twitter data.

Contribution

It compares shallow and deep learning models for ADHD detection, emphasizing interpretability and cross-platform generalizability of linguistic features.

Findings

BiLSTM balances transparency and accuracy

Models generalize well across Reddit and Twitter

Key linguistic features linked to ADHD identified

Abstract

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent mental health condition affecting both children and adults, yet it remains severely underdiagnosed. Recent advances in artificial intelligence, particularly in Natural Language Processing (NLP) and Machine Learning (ML), offer promising solutions for scalable and non-invasive ADHD screening methods using social media data. This paper presents a comprehensive study on ADHD detection, leveraging both shallow machine learning models and deep learning approaches, including BiLSTM and transformer-based models, to analyze linguistic patterns in ADHD-related social media text. Our results highlight the trade-offs between interpretability and performance across different models, with BiLSTM offering a balance of transparency and accuracy. Additionally, we assess the generalizability of these models using cross-platform data from Reddit and Twitter, uncovering key linguistic features associated with ADHD that could contribute to more effective digital screening tools.