Comparaison de l’analyse thématique qualitative et de la modélisation de sujets fondée sur des machines dans l’analyse des récits des émotions de jeunes autistes et avec TDAH

Titre original en anglais : Comparing qualitative thematic analysis and machine-based topic modelling in the analysis of autistic and ADHD young people’s accounts of emotions

Cette publication est incluse dans le projet « Contributions académiques de personnes autistes sur l’autisme ». trois auteur·ices de cette publication sont identifié·es comme autistes dans le projet.

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Lukito, S., Li, L., Chandler, S., Kakoulidou, M., Pavlopoulou, G., Matejko, M., Jackson, I., Balwani, B., Boyens, T., Poulton, D., Harvey-Nguyen, L., Johnson, A., Stahl, D., Roberts, A., Sonuga-Barke, E. J., team, t. R., Sonuga-Barke, E. J., Chandler, S., Danese, A.,..., & Wilson, A. (2026). Comparing qualitative thematic analysis and machine-based topic modelling in the analysis of autistic and ADHD young people’s accounts of emotions. Scientific Reports, 16(1), Article 4615. https://doi.org/10.1038/s41598-025-34570-7

Date de publication: 28/01/2026 Ajout dans AutiHub: 09/10/2026 Type: Article Langue de l’article: Anglais
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Auteurs

Auteur·ices des publications
45
Auteur·ices de la publication identifié·es comme autistes
3 / 45 (6,7 %)
  1. Steve Lukito
  2. Lifang Li
  3. Susie Chandler
  4. Myrofora Kakoulidou
  5. Georgia Pavlopoulou
  6. Maciej Matejko *
  7. Isabel Jackson
  8. Beta Balwani
  9. Tiegan Boyens
  10. Dorian Poulton
  11. Luke Harvey-Nguyen
  12. Amber Johnson *
  13. Daniel Stahl
  14. Angus Roberts
  15. Edmund J.S. Sonuga-Barke
  1. the RE‐STAR team
  2. Edmund J.S. Sonuga-Barke
  3. Susie Chandler
  4. Andrea Danese
  5. Johnny M. Downs
  6. Eloise Funnell
  7. Kirsty Griffiths
  8. Myrofora Kakoulidou
  9. Lauren Low
  10. Umaya Prasad
  11. Emily Simonoff
  12. Anna Wyatt
  13. Georgia Pavlopoulou
  14. Jane Hurry
  15. Sylvan Baker
  1. Graham Moore
  2. Dennis Ougrin
  3. Amanda Roestorf
  4. Rebecca Kirkbride
  5. Jordan Altimimi
  6. Saskia Barnes
  7. Zoë Glen *
  8. C. J. Harris
  9. Charlotte Hillman
  10. Issy Jackson
  11. Elisa Ly
  12. Elizabeth Macauley
  13. Anya Rose
  14. Darren Webb
  15. Archie Wilson

Résumé

Résumé L’analyse systématique de données d’entretien peut fournir des éclairages importants sur la manière dont les jeunes vivent et interprètent leurs émotions. Des approches analytiques qualitatives dirigées par des humains (p. ex., l’analyse thématique) et quantitatives pilotées par des machines (p. ex., le traitement automatique du langage naturel [NLP]) sont toutes deux disponibles, mais leurs solutions sont rarement comparées. Les réponses d’entretien de 57 adolescents neurodivergents à des questions concernant leurs émotions, précédemment analysées au moyen d’une analyse thématique réflexive (RTA), ont été soumises à une modélisation de sujets (TM). Les étiquettes de sujets ont été élaborées en collaboration avec des cochercheurs neurodivergents afin d’ancrer leur signification dans l’expérience vécue des communautés neurodivergentes. Les sujets ont été mis en correspondance avec des thèmes ou sous-thèmes de la RTA afin d’examiner leurs proximités. Une analyse de grappes fondée sur les sujets a été utilisée pour identifier des regroupements de participants présentant des distributions de sujets similaires. La TM a révélé 10 sujets interprétables et significatifs liés aux émotions, certains se chevauchant étroitement avec les thèmes de la RTA et d’autres en différant. Les sujets de la TM ont différencié les expériences émotionnelles des jeunes à l’école de celles vécues dans d’autres contextes. La TM et la RTA ont produit des éclairages à la fois convergents et différents sur la signification des récits des jeunes neurodivergents concernant leurs émotions. Nos résultats démontrent l’utilisation potentielle de la TM dans l’analyse d’entretiens et pourraient suggérer une complémentarité potentielle entre les sujets de la TM et les thèmes de la RTA, à explorer davantage au moyen d’algorithmes plus avancés et d’une mise en oeuvre plus sophistiquée du NLP.

Abstract Systematic analysis of interview data can provide important insights into how young people experience and interpret their emotions. Both human-led qualitative (e.g., thematic analysis) and machine-driven quantitative (e.g., natural language processing [NLP]) analytical approaches are available, but their solutions are rarely compared. Interview responses by 57 neurodivergent adolescents to questions about their emotions, previously analysed using reflexive thematic analysis (RTA), were submitted to Topic Modelling (TM). Topic labels were developed in collaboration with neurodivergent co-researchers to ground their meaning in the lived experience of neurodivergent communities. Topics were mapped to RTA themes or subthemes to examine their proximities. Topic-based cluster analysis was used to identify participant groupings with similar topic distributions. TM revealed 10 interpretable and meaningful emotion-related topics – some closely overlapping with and others differing from the RTA themes. TM topics differentiated the young people’s emotional experiences at school from those in other settings. TM and RTA resulted in overlapping and different insights into the meaning of neurodivergent young people’s accounts of their emotions. Our findings demonstrate the potential use of TM in interview analysis and might suggest a potential complementarity between the TM topics and RTA themes, to be further explored using more advanced algorithms and a more sophisticated NLP implementation.

Bibliographie citée par cette référence

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Vue d’ensemble de la bibliographie citée

Ces indicateurs décrivent la bibliographie citée par cette publication. Un nom d’auteurice est compté chaque fois qu’il apparaît dans une référence citée : une même personne peut donc être comptée plusieurs fois. Les noms qui ne sont pas encore associés à un·e auteurice déjà présent·e dans AutiHub sont traités comme inconnus, pas comme non autistes. Dernier calcul : 17/10/2026 01:01.

Références citées
72
Avec un DOI
54
Sans DOI, à partir du texte brut de la bibliographie
18
4 / 72 (5,6 %) références citées comprennent au moins une personne identifiée comme autiste.
Références avec données à compléter
11 / 72 (15,3 %)
Références avec noms d’auteurices détectés
67 / 72 (93,1 %)
Sans nom d’auteurice détecté
5
Sans titre structuré
11
Sans identifiant stable
18
Références avec noms bruts d’auteurices restant à vérifier
5
Références avec problème de récupération des métadonnées externes
0
Ces indicateurs portent sur les références citées affichées sur cette page, après fusion des doublons techniques. Une référence sans DOI peut quand même soutenir les statistiques d’auteurices lorsqu’un titre et des noms d’auteurices sont disponibles.
Noms d’auteurices détectés dans la bibliographie citée
252
À partir du DOI ou de métadonnées externes
252
À partir du texte brut validé de la bibliographie
0
Noms bruts déjà validés
0
Noms bruts restant à vérifier
5
82 / 252 (32,5 %) noms sont associés à un·e auteurice déjà présent·e dans AutiHub. 170 / 252 (67,5 %) noms ne sont pas encore associés.
Noms associés à une personne identifiée comme autiste
5 / 252 (2,0 %)
Calculé sur l’ensemble des noms d’auteurices détectés dans la bibliographie citée. Parmi les noms associés à un·e auteurice déjà présent·e dans AutiHub : 5 / 82 (6,1 %). Personnes distinctes identifiées comme autistes : 4 / 207 (1,9 %).
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