ArticleJournal of personalized medicine2022
Social Media and Chronic Pain: What Do Patients Discuss?
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- A scoping review of applications of natural language processing for chronic pain research.Discover artificial intelligence · 2026Review
- The expectations we build: reframing chronic pain outcomes as a systems level public health issue.Frontiers in public health · 2026Article
- What makes life go well? A network topic modeling analysis of well-being practices in adults with chronic pain.Pain medicine (Malden, Mass.) · 2025Article
- Peer Support for Chronic Pain in Online Health Communities: Quantitative Study on the Dynamics of Social Interactions in a Chronic Pain Forum.Journal of medical Internet research · 2024Article
- The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain.Current pain and headache reports · 2024Review
- Understanding chronic pain in the ubiquitous community: the role of open data.Frontiers in pain research (Lausanne, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.