Affiliation:
1. School of Clinical Medicine University of Cambridge Cambridge UK
2. Cambridge University Hospitals NHS Foundation Trust Cambridge UK
3. Department of Otolaryngology–Head and Neck Surgery Addenbrooke's Hospital Cambridge UK
Abstract
AbstractObjectiveThere is a paucity of research examining patient experiences of cochlear implants. We sought to use natural language processing methods to explore patient experiences and concerns in the online cochlear implant (CI) community.Materials and MethodsCross‐sectional study of posts on the online Reddit r/CochlearImplants forum from 1 March 2015 to 11 November 2021. Natural language processing using the BERTopic automated topic modelling technique was employed to cluster posts into semantically similar topics. Topic categorisation was manually validated by two independent reviewers and Cohen's kappa calculated to determine inter‐rater reliability between machine vs human and human vs human categorisation.ResultsWe retrieved 987 posts from 588 unique Reddit users on the r/CochlearImplants forum. Posts were initially categorised by BERTopic into 16 different Topics, which were increased to 23 Topics following manual inspection. The most popular topics related to CI connectivity (n = 112), adults considering getting a CI (n = 107), surgery‐related posts (n = 89) and day‐to‐day living with a CI (n = 85). Cohen's kappa among all posts was 0.62 (machine vs. human) and 0.72 (human vs. human), and among categorised posts was 0.85 (machine vs. human) and 0.84 (human vs. human).ConclusionsThis cross‐sectional study of social media discussions among the online cochlear implant community identified common attitudes, experiences and concerns of patients living with, or seeking, a cochlear implant. Our validation of natural language processing methods to categorise topics shows that automated analysis of similar Otolaryngology‐related content is a viable and accurate alternative to manual qualitative approaches.