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Updated: May 21, 2025
Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma
Published on: June 10, 2017
Leveraging social media data to study disease and treatment characteristics of Hodgkin's lymphoma Using Natural
Zasim Azhar Siddiqui1, Maryam Pathan1, Sabina Nduaguba1
1Department of Pharmaceutical Systems and Policy, School of Pharmacy, West Virginia University, Morgantown, West Virginia, United States of America.
Background:
The use of social media platforms in health research is increasing, yet their application in studying rare diseases is limited. Hodgkin's lymphoma (HL) is a rare malignancy with a high incidence in young adults. This study evaluates the feasibility of using social media data to study the disease and treatment characteristics of HL.
Methods:
We utilized the X (formerly Twitter) API v2 developer portal to download posts (formerly tweets) from January 2010 to October 2022. Annotation guidelines were developed from literature and a manual review of limited posts was performed to identify the class and attributes (characteristics) of HL discussed on X, and create a gold standard dataset. This dataset was subsequently employed to train, test, and validate a Named Entity Recognition (NER) Natural Language Processing (NLP) application.
Results:
After data preparation, 80,811 posts were collected: 500 for annotation guideline development, 2,000 for NLP application development, and the remaining 78,311 for deploying the application. We identified nine classes related to HL, such as HL classification, etiopathology, stages and progression, and treatment. The treatment class and HL stages and progression were the most frequently discussed, with 20,013 (25.56%) posts mentioning HL's treatments and 17,177 (21.93%) mentioning HL stages and progression. The model exhibited robust performance, achieving 86% accuracy and an 87% F1 score. The etiopathology class demonstrated excellent performance, with 93% accuracy and a 95% F1 score.
Discussion:
The NLP application displayed high efficacy in extracting and characterizing HL-related information from social media posts, as evidenced by the high F1 score. Nonetheless, the data presented limitations in distinguishing between patients, providers, and caregivers and in establishing the temporal relationships between classes and attributes. Further research is necessary to bridge these gaps.
Conclusion:
Our study demonstrated potential of using social media as a valuable preliminary research source for understanding the characteristics of rare diseases such as Hodgkin's Lymphoma.
