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Conference Papers Year : 2024

Harnessing Linguistic Analysis for ADHD Diagnosis Support


This study explores the potential of stylometric analysis in identifying Self-Defining Memories (SDMs) authored by individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) versus a control group. A sample of 198 SDMs were written by 66 adolescents and were then analysed using Support Vector Classifiers (SVC). The analysis included a variety of linguistic features such as character 3-grams, function words, sentence length, or lexical richness among others. It also included metadata about the participants (gender, age) and their SDMs (self-reported sentiment after recalling their memories). The results reveal a promising ability of linguistic analysis to accurately classify SDMs, with perfect prediction (F1=1.0) in the contextually simpler setup of text-by-text prediction, and satisfactory levels of precision (F1 = 0.77) when predicting individual by individual. Such results highlight the significant role that linguistic characteristics play in reflecting the distinctive cognitive patterns associated with ADHD. While not a substitute for professional diagnosis, textual analysis offers a supportive avenue for early detection and a deeper understanding of ADHD.
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hal-04558160 , version 1 (24-04-2024)





Florian Cafiero, Juan Barrios, Simon Gabay, Martin Debbané. Harnessing Linguistic Analysis for ADHD Diagnosis Support: A Stylometric Approach to Self-Defining Memories. RaPID-5@LREC-COLING-2024, Association for Computational Linguistics, May 2024, Turin, Italy. ⟨10.31234/osf.io/m97gx⟩. ⟨hal-04558160⟩


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