Detecting ADHD through Natural Language Processing and Stylometric Analysis of Adolescent Narratives

Juan Barrios Rudloff^1*Elena Poznyak¹Jessica Lee Samson¹Halima Rafi¹Simon Gabay²Florian Cafiero³Martin Debbané^1,4

• ¹Developmental Clinical Psychology Research Unit, Faculty of Psychology and Educational Sciences, University of Geneva, Geneva, Switzerland
• ²Faculty of Humanities, University of Geneva, Geneva, Switzerland
• ³Ecole nationale des chartes, PSL, Paris, France
• ⁴Research Department of Clinical, Educational and Health Psychology, University College London, London, United Kingdom

Attention-Deficit/Hyperactivity Disorder (ADHD) significantly affects adolescents' everyday lives, particularly in emotion regulation and interpersonal relationships. Despite its high prevalence, ADHD remains underdiagnosed, highlighting the need for improved diagnostic tools. This study explores, for the first time, the potential of Natural Language Processing (NLP) and stylometry to identify linguistic markers within Self-Defining Memories (SDMs) of adolescents with ADHD and to evaluate their utility in detecting the disorder. A further novel aspect of this research is the use of SDMs as a linguistic dataset, which reveals meaningful patterns while engaging psychological processes related to identity and memory. Our objectives were to: (1) characterize linguistic features of SDMs in ADHD and control groups; (2) assess the predictive power of stylometry for group classification, distinguishing between controls and individuals with ADHD; and (3) conduct a qualitative analysis of key linguistic markers of each group. Twenty-five adolescents diagnosed with ADHD and forty-one typically developing adolescents participated in the study. The results showed that adolescents with ADHD produced shorter, less diverse, and less cohesive narratives. In addition, stylometric analysis using Support Vector Machine (SVM) classifiers distinguished between ADHD and control groups with up to 100% precision. Distinct linguistic markers were identified, potentially reflecting challenges in emotion regulation. These findings suggest that NLP and stylometry can enhance ADHD diagnostics by providing objective linguistic markers, thereby improving both its understanding and diagnostic procedures. Further research is needed to validate these methods in larger and more diverse populations.