{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T00:52:04Z","timestamp":1767919924859,"version":"3.49.0"},"reference-count":72,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T00:00:00Z","timestamp":1742342400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>\nBackground\n<\/jats:title>\n<jats:p>The use of social media platforms in health research is increasing, yet their application in studying rare diseases is limited. Hodgkin\u2019s 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.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>\nMethods\n<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>\nResults\n<\/jats:title>\n<jats:p>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\u2019s 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.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>\nDiscussion\n<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec005\">\n<jats:title>\nConclusion\n<\/jats:title>\n<jats:p>Our study demonstrated potential of using social media as a valuable preliminary research source for understanding the characteristics of rare diseases such as Hodgkin\u2019s Lymphoma.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pdig.0000765","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T18:12:36Z","timestamp":1742407956000},"page":"e0000765","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["Leveraging social media data to study disease and treatment characteristics of Hodgkin\u2019s lymphoma Using Natural Language Processing methods"],"prefix":"10.1371","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6719-3228","authenticated-orcid":true,"given":"Zasim 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