Due to the high economic and public health burden of chronic pain, and the risk of public health consequences of opioid-based treatments, there is a need to identify effective alternative therapies. The evidence basis for many alternative therapies is weak or nonexistent. Social media presents a unique opportunity to gather large-scale knowledge about such therapies self-reported by sufferers themselves. We attempted to (i) verify the presence of largescale chronic pain-related chatter on Twitter, (ii) develop natural language processing (NLP) and machine learning for automatically detecting chronic pain sufferers, and (iii) identify the types of chronic pain-related information reported by them. We collected data from Twitter using chronic pain-related hashtags and keywords. We manually performed binary annotation of a sample of 4998 posts to indicate if they were self-reports of chronic pain experiences or not, and obtained inter-annotator agreement of 0.82 (Cohen's kappa). We trained and evaluated several state-of-the-art transformer-based text classification models using the annotated data. The RoBERTa model outperformed all others (F1 score = 0.84; 95% CI: 0.80-0.89), and we used this model to classify a large number of unlabeled posts. We identified 22,795 self-reported chronic pain sufferers and collected their past posted data. Via manual and NLP-driven analyses, we found information about but not limited to alternative treatments, sufferers' sentiments about treatments, side effects, and self-management strategies. Our social media-based approach will result in an automatically growing massive cohort over time, and the data can be leveraged to identify self-reported effective alternative therapies for diverse chronic pain types.

The authors have declared no competing interest.

Research reported in this publication was supported in part by the National Institute on Drug Abuse (NIDA) of the National Institutes of Health (NIH) under award number R01DA046619. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH/NIDA.

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