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

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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

Publication date: 28 Jan 2026 Added to AutiHub: 9 Oct 2026 Type: Article Article language: English
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Authors

Publication authors
45
Publication authors identified as autistic
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

Abstract

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.

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Cited references
72
With a DOI
54
Without a DOI, from raw bibliography text
18
4 / 72 (5.6%) cited references include at least one author identified as autistic.
References with data to complete
11 / 72 (15.3%)
References with detected author names
67 / 72 (93.1%)
Without detected author names
5
Without a structured title
11
Without a stable identifier
18
References with raw author names still to review
5
References with external metadata lookup issues
0
These indicators apply to cited references displayed on this page, after technical duplicates have been merged. A reference without a DOI can still support author statistics when a title and author names are available.
Author names detected in the cited bibliography
252
From DOI or external metadata
252
From validated raw bibliography text
0
Raw names already validated
0
Raw names still to review
5
82 / 252 (32.5%) names are linked to an author already present in AutiHub. 170 / 252 (67.5%) names are not yet linked.
Names linked to a person identified as autistic
5 / 252 (2.0%)
Calculated across all author names detected in the cited bibliography. Among names linked to an author already present in AutiHub: 5 / 82 (6.1%). Distinct people identified as autistic: 4 / 207 (1.9%).
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