AUTHOR=Towler Lauren , Bondaronek Paulina , Papakonstantinou Trisevgeni , Amlôt Richard , Chadborn Tim , Ainsworth Ben , Yardley Lucy 

TITLE=Applying machine-learning to rapidly analyze large qualitative text datasets to inform the COVID-19 pandemic response: comparing human and machine-assisted topic analysis techniques

JOURNAL=Frontiers in Public Health

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2023.1268223

DOI=10.3389/fpubh.2023.1268223

ISSN=2296-2565

ABSTRACT=Machine-assisted topic analysis (MATA) uses artificial intelligence methods to help qualitative researchers analyse large datasets. This is useful for researchers to rapidly update healthcare interventions during changing healthcare contexts, such as a pandemic. We examined the potential to support healthcare interventions by comparing MATA with 'human-only' thematic analysis techniques on the same dataset (1472 user responses from a COVID-19 behavioural intervention).In MATA, an unsupervised topic-modelling approach identified latent topics in the text, from which researchers identified broad themes. In human-only codebook analysis, researchers developed an initial codebook based on previous research that was applied to the dataset by the team, who met regularly to discuss and refine the codes. Formal triangulation using a 'convergence coding matrix' compared findings between methods, categorising them as 'agreement', 'complementary', 'dissonant', or 'silent'.Human analysis took much longer than MATA (147.5 vs. 40 hours). Both methods identified key themes about what users found helpful and unhelpful. Formal triangulation showed both sets of findings were highly similar. The formal triangulation showed high similarity between the findings. All MATA codes were classified as in agreement or complementary to the human themes. When findings differed slightly, this was due to human researcher interpretations or nuance from human-only analysis.Results produced by MATA were similar to human-only thematic analysis, with substantial time savings. For simple analyses that do not require an in-depth or subtle understanding of the data, MATA is a useful tool that can support qualitative researchers to interpret and analyse large datasets quickly. This approach can support intervention development and implementation, such as enabling rapid optimisation during public health emergencies.