This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility:



Automated chronic pain cohort mined from Twitter using machine learning

Automated chronic pain cohort mined from Twitter using machine learning
End-to-end data processing framework involving data collection/storage via the Twitter API, data preparation and preprocessing, training, evaluation, and classification using the final model and postclassification analyses. Credit: Health Data Science (2023). DOI: 10.34133/hds.0078

Social media platforms like Twitter (now X) offer valuable insights into self-reported chronic pain, according to a recent study by multiple institutions. The researchers have automated the process of establishing a chronic pain cohort, setting the stage for future data mining and causal association studies.

The study was published in Health Data Science.

"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," said Abeed Sarker, associate professor at Emory University.

Chronic pain and subsequent opioid use pose significant health and economic challenges. Gathering and experiences could guide alternative therapies for specific pain types.

The team collected data via Twitter's academic API, followed by manual annotation and classification.

For instance, posts mentioned therapies like meditation and chiropractic, and sentiment analysis revealed varying public opinions on these therapies.

"Social media is a rich source for chronic pain-related information. Methods like NLP and machine learning can extract key insights from this large dataset," explained Prof. Sarker.

The study's automation features include detecting relevant posts and identifying self-reported cases, enabling the inclusion of individuals usually not reachable through traditional research settings.

Social media-based cohorts may also lend a unique perspective to chronic pain. For example, subscribers' social support (e.g., numbers of followers and interactions with a given post), its influence on their quality of life, and the connection between and social aspects of living.

This is the first publication that builds an elaborate NLP and pipeline to curate knowledge about chronic pain from patient-generated data. The automated collection of cohort members via classification and their posts over time will yield various hypotheses about chronic pain control and an unprecedented resource for long-term studies on the topic.

"The next step will be to do a more comprehensive analysis of the data posted by our chronic pain cohort, particularly to generate hypotheses about effective opioid alternatives for specific types," said Prof. Sarker.

More information: Abeed Sarker et al, #ChronicPain: Automated Building of a Chronic Pain Cohort from Twitter Using Machine Learning, Health Data Science (2023). DOI: 10.34133/hds.0078

Provided by Health Data Science
Citation: Automated chronic pain cohort mined from Twitter using machine learning (2023, October 10) retrieved 4 March 2024 from
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.

Explore further

A new model to identify and predict chronic pain


Feedback to editors