Exploring patient experiences and concerns in the online Cochlear Implant community: a natural language processing approach

Importance: There is a paucity of research examining patient experiences of cochlear implants, with existing studies limited by small sample sizes and closed question-answer style formats. Objective: To use natural language processing methods to explore patient experiences and concerns in the online cochlear implant (CI) community when in conversation with each other. Design: Cross-sectional study of the online Reddit r/CochlearImplants forum. No date restrictions were imposed; data were retrieved on 11 November 2021 and analysed between November 2021 and March 2022. The following details were extracted for each post: 1) original post content, 2) post comments, and 3) metadata for the original post and user. Setting: Online Reddit forum r/CochlearImplants. Participants: Consecutive sample of all users posting on the r/CochlearImplants forum from 1 March 2015 to 11 November 2021. Main Outcomes and Measures: 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. Results: We 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 22 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). Topics with the most comments included Choosing cochlear implant brand (mean = 12.9 comments) and Adults considering getting a CI (mean = 12.2 comments). 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). Conclusions and Relevance: This cross-sectional study of social media discussions amongst 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.