Reported experiences of chronic pain may convey qualities relevant to the exploration of this private and subjective experience. We propose this exploration by means of the Reddit Reports of Chronic Pain (RRCP) dataset. We define and validate the RRCP for a set of subreddits related to chronic pain, identify the main concerns discussed in each subreddit, model each subreddit according to their main concerns, and compare subreddit models. The RRCP dataset comprises 86,537 submissions from 12 subreddits related to chronic pain (each related to one pathological background). Each RRCP subreddit was found to have various main concerns. Some of these concerns are shared between multiple subreddits (e.g., the subreddit Sciatica semantically entails the subreddit backpain in their various concerns, but not the other way around), whilst some concerns are exclusive to specific subreddits (e.g., Interstitialcystitis and CrohnsDisease). Our analysis details each of these concerns and their (dis)similarity relations. Although limited by the intrinsic qualities of the Reddit platform, to the best of our knowledge, this is the first research work attempting to model the linguistic expression of various chronic pain-inducing pathologies and comparing these models to identify and quantify the similarities and differences between the corresponding emergent, chronic pain experiences.

Background: Placebo analgesia is a widely studied clinical phenomenon, yet placebo responses vary widely across individuals. Prior research has identified biopsychosocial factors that determine the likelihood of an individual to respond to placebo, yet generalizability and ecological validity in those studies have been limited due to the inability to account for dynamic personal and treatment effects. Methods: We assessed fine‐tuned large language models (LLMs) for the prediction of placebo responses in chronic low‐back pain using contextual features extracted from patient interviews, as they speak about their lifestyle, pain, and treatment history. Interviews were conducted as part of two RCTs designed to study the placebo effect. These interviews were collected after treatment in the first trial (discovery cohort) and prior to treatment in the second trial (validation cohort). Results: Semantic features extracted with LLMs can predict which individuals respond to a placebo, with an accuracy of 74% in unseen data, and validating with 70% accuracy in an independent cohort. Furthermore, in contrast to previous work, LLMs eliminated the need for pre‐selecting search terms, enabling a fully data‐driven approach, and provided interpretable insights into psychosocial factors underlying placebo responses. Conclusions: These findings expand on prior research by integrating state‐of‐art NLP techniques to address limitations in interpretability and context sensitivity of the traditional methods in related work. This method highlights the role of language models to link language and psychological states, paving the way for a deeper quantitative exploration of biopsychosocial phenomena, and to understand how they relate to treatment outcomes. Significance Statement: This study paves the way for a deeper yet quantitative exploration of biopsychosocial phenomena through language, and to understand how they relate to treatment outcomes, namely placebo. In this case it highlights nuanced linguistic patterns linked to responder status, which tap into semantic dimensions such as “anxiety,” “resignation,” and “hope”.

Placebo analgesia in chronic pain is a widely studied clinical phenomenon, where expectations about the effectiveness of a treatment can result in substantial pain relief when using an inert treatment agent. While placebos offer an opportunity for non-pharmacological treatment in chronic pain, not everyone demonstrates an analgesic response. Prior research has identified biopsychosocial factors that determine the likelihood of an individual to respond to a placebo, yet generalizability and ecological validity in those studies have been limited due to the inability to account for dynamic personal and treatment effects—which are well-known to play a role. Here, we assessed the potential of using fine-tuned large language models (LLMs) to predict placebo responders in chronic low-back pain using contextual features extracted from patient interviews, as they speak about their lifestyle, pain, and treatment history. We re-analyzed data from two clinical trials where individuals performed open-ended interviews and used these to develop a predictive model of placebo response. Our findings demonstrate that semantic features extracted with LLMs accurately predicted placebo responders, achieving a classification accuracy of 74% in unseen data, and validating with 70% accuracy in an independent cohort. Further, LLMs eliminated the need for pre-selecting search terms or to use dictionary approaches, enabling a fully data-driven approach. This LLM method further provided interpretable insights into psychosocial factors underlying placebo responses, highlighting nuanced linguistic patterns linked to responder status, which tap into semantic dimensions such as “anxiety,” “resignation,” and “hope.” These findings expand on prior research by integrating state-of-art NLP techniques to address limitations in interpretability and context sensitivity of standard methods like bag-of-words and dictionary-based approaches. This method highlights the role of language models to link language and psychological states, paving the way for a deeper yet quantitative exploration of biopsychosocial phenomena, and to understand how they relate to treatment outcomes, including placebo.

Objective: Reveal and quantify qualities of reported experiences of chronic pain on social media, from multiple pathological backgrounds, by means of the novel Reddit Reports of Chronic Pain (RRCP) dataset, using Natural Language Processing techniques. Materials and Methods: Define and validate the RRCP dataset for a set of subreddits related to chronic pain. Identify the main concerns discussed in each subreddit. Model each subreddit according to their main concerns. Compare subreddit models. Results: The RRCP dataset comprises 86,537 Reddit submissions from 12 subreddits related to chronic pain (each related to one pathological background). Each RRCP subreddit has various main concerns. Some of these concerns are shared between multiple subreddits (e.g., the subreddit Sciatica semantically entails the subreddit backpain in their various concerns, but not the other way around), whilst some concerns are exclusive to specific subreddits (e.g., Interstitialcystitis and CrohnsDisease). Discussion: These results suggest that the reported experience of chronic pain, from multiple pathologies (i.e., subreddits), has concerns relevant to all, and concerns exclusive to certain pathologies. Our analysis details each of these concerns and their similarity relations. Conclusion: Although limited by intrinsic qualities of the Reddit platform, to the best of our knowledge, this is the first research work attempting to model the linguistic expression of various chronic pain-inducing pathologies and comparing these models to identify and quantify the similarities and differences between the corresponding emergent chronic pain experiences.