@inproceedings{cafiero-etal-2024-harnessing,
title = "Harnessing Linguistic Analysis for {ADHD} Diagnosis Support: A Stylometric Approach to Self-Defining Memories",
author = {Cafiero, Florian Rapha{\"e}l and
Barrios Rudloff, Juan and
Gabay, Simon},
editor = "Kokkinakis, Dimitrios and
Fraser, Kathleen C. and
Themistocleous, Charalambos K. and
Fors, Kristina Lundholm and
Tsanas, Athanasios and
Ohman, Fredrik",
booktitle = "Proceedings of the Fifth Workshop on Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments @LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.rapid-1.10",
pages = "87--94",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Harnessing Linguistic Analysis for ADHD Diagnosis Support: A Stylometric Approach to Self-Defining Memories
%A Cafiero, Florian Raphaël
%A Barrios Rudloff, Juan
%A Gabay, Simon
%Y Kokkinakis, Dimitrios
%Y Fraser, Kathleen C.
%Y Themistocleous, Charalambos K.
%Y Fors, Kristina Lundholm
%Y Tsanas, Athanasios
%Y Ohman, Fredrik
%S Proceedings of the Fifth Workshop on Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments @LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F cafiero-etal-2024-harnessing
%X 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.
%U https://aclanthology.org/2024.rapid-1.10
%P 87-94
Markdown (Informal)
[Harnessing Linguistic Analysis for ADHD Diagnosis Support: A Stylometric Approach to Self-Defining Memories](https://aclanthology.org/2024.rapid-1.10) (Cafiero et al., RaPID-WS 2024)
ACL