Social media platforms, including X (formerly known as Twitter), have become valuable tools for studying and understanding self-reported chronic pain, according to a recent research effort conducted by multiple institutions. The study focused on automating processes to create a cohort of individuals with chronic pain, opening up possibilities for future data mining and causal association investigations.
The study’s goals were articulated by Abeed Sarker, an associate professor at Emory University. He stated, “We aimed to detect large-scale discussions related to chronic pain on Twitter, develop methods for automatic detection of self-disclosures, and collect and analyze longitudinal data.”
Chronic pain, a condition often linked to opioid use, poses significant challenges to public health and the economy. By aggregating public insights and experiences, researchers hope to guide alternative therapies for specific types of pain.
The researchers collected data using Twitter’s academic API and manually annotated and classified the posts. This included posts discussing therapies such as meditation and chiropractic care. Sentiment analysis revealed diverse public opinions on these treatments.
Sarker noted that social media platforms can be a rich source of chronic pain-related information. He emphasized the use of natural language processing (NLP) and machine learning to extract key insights from such a large dataset.
The automation capabilities of the study allow for the identification of relevant posts and instances of self-reporting. This enables the inclusion of individuals who may not typically participate in conventional research settings.
Cohorts established through social media channels provide a unique perspective on chronic pain, including the social support received by subscribers and its influence on their quality of life. Additionally, these cohorts help explore the correlation between alternative therapies and the social dimensions of daily life.
The study’s abstract states, “Our social media-based approach will result in an automatically growing large cohort over time, and the data can be leveraged to identify effective opioid-alternative therapies for diverse chronic pain types.”
This research publication stands as the first to construct a comprehensive framework for accumulating chronic pain knowledge from patient-generated social media content using NLP and machine learning. The automated assembly of cohort members and the longitudinal analysis of their posts are expected to generate hypotheses regarding chronic pain management.
The next steps include a more comprehensive analysis of the data posted by the chronic pain cohort in order to generate hypotheses about effective opioid alternatives for specific chronic pain types.
The study concludes by highlighting the potential of the social media-based approach in creating a continuously expanding cohort and identifying effective opioid-alternative therapies for diverse chronic pain types. The research team’s findings have been published in the journal Health Data Science.
I have over 10 years of experience in the cryptocurrency industry and I have been on the list of the top authors on LinkedIn for the past 5 years. I have a wealth of knowledge to share with my readers, and my goal is to help them navigate the ever-changing world of cryptocurrencies.