A high number of online support groups have been created on social media platforms to reinforce personal empowerment and social support. The goal of this study was to perform natural language processing by constructing a bag-of-words model and conducting topic modelling based on posts extracted from a chronic pain community. The subreddit called 'r/sChronicPain' was used to investigate communication on social media platforms for chronic pain patients. After data cleaning and lemmatisation, a word cloud was constructed, and the most frequent words and most frequent body regions were counted. Latent Dirichlet allocation was used to perform topic modelling. In the final analysis set, 937 unique posts were included. The most frequent word was 'pain', followed by 'doctor', 'day', 'feel', 'back', 'year', and 'time'. Concerning the body regions, 'back' was most often mentioned, followed by 'neck' and 'leg'. Based on coherence scores, one topic was extracted with 'pain' as the keyword with the highest weight. In line with the allocation of chronic low-back pain as a major health problem and increasing prevalence, back pain was most often mentioned. It seems that the primarily treatment trajectories that are proposed by medical physicians are discussed on social media, compared to interventions by other healthcare providers.
Keywords: chronic pain; natural language processing; patient opinion; qualitative analysis; social media communities.