Transparent but Powerful: Explainability, Accuracy, and Generalizability in ADHD Detection from Social Media Data
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912131181445120 |
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| author | Wiechmann, D. Kempa, E. Kerz, E. Qiao, Y. |
| author_facet | Wiechmann, D. Kempa, E. Kerz, E. Qiao, Y. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_15586 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transparent but Powerful: Explainability, Accuracy, and Generalizability in ADHD Detection from Social Media Data Wiechmann, D. Kempa, E. Kerz, E. Qiao, Y. Computation and Language 68T50 I.2.7; I.5.1 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. |
| title | Transparent but Powerful: Explainability, Accuracy, and Generalizability in ADHD Detection from Social Media Data |
| topic | Computation and Language 68T50 I.2.7; I.5.1 |
| url | https://arxiv.org/abs/2411.15586 |