Impact of the COVID-19 Pandemic and the 2021 National Institute for Health and Care Excellence Guidelines on Public Perspectives Toward Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Thematic and Sentiment Analysis on Twitter (Rebranded as X)
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| Udgivet i: | Journal of Medical Internet Research vol. 27 (2025), p. e65087 |
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| Andre forfattere: | , , , , , , , |
| Udgivet: |
Gunther Eysenbach MD MPH, Associate Professor
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| Online adgang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.2196/65087 |2 doi | |
| 035 | |a 3222368764 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Khakban, Iliya | |
| 245 | 1 | |a Impact of the COVID-19 Pandemic and the 2021 National Institute for Health and Care Excellence Guidelines on Public Perspectives Toward Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Thematic and Sentiment Analysis on Twitter (Rebranded as X) | |
| 260 | |b Gunther Eysenbach MD MPH, Associate Professor |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Background:Myalgic encephalomyelitis (ME), also referred to as chronic fatigue syndrome (CFS), is a complex illness that typically presents with disabling fatigue and cognitive and functional impairment. The etiology and management of ME/CFS remain contentious and patients often describe their experiences through social media.Objective:We explored public discourse on Twitter (rebranded as X) to understand the concerns and priorities of individuals living with ME/CFS, with a focus on (1) the COVID-19 pandemic and (2) publication of the 2021 UK National Institute for Health and Care Excellence (NICE) guidelines on the diagnosis and management of ME/CFS.Methods:We used the Twitter application programming interface to collect tweets related to ME/CFS posted between January 1, 2010, and January 30, 2024. Tweets were sorted into 3 chronological periods (pre–COVID-19 pandemic, post–COVID-19 pandemic, and post-UK 2021 NICE Guidelines publication). A Robustly Optimized Bidirectional Embedding Representations from Transformers Pretraining Approach (RoBERTa) language processing model was used to categorize the sentiment of tweets as positive, negative, or neutral. We identified tweets that mentioned COVID-19, the UK NICE guidelines, and key themes identified through latent Dirichlet allocation (ie, fibromyalgia, research, and treatment). We sampled 1000 random tweets from each theme to identify subthemes and representative quotes.Results:We retrieved 906,404 tweets, of which 427,824 (47.2%) were neutral, 369,371 (40.75%) were negative, and 109,209 (12.05%) were positive. Over time, both the proportion of negative and positive tweets increased, and the proportion of neutral tweets decreased (P<.001 for all changes). Tweets mentioning fibromyalgia acknowledged similarities with ME/CFS, stigmatization associated with both disorders, and lack of effective treatments. Treatment-related tweets often described frustration with ME/CFS labeled as mental illness, dismissal of concerns by health care providers, and the need to seek out “good physicians” who viewed ME/CFS as a physical disorder. Tweets on research typically praised studies of biomarkers and biomedical therapies, called for greater investment in biomedical research, and expressed frustration with studies suggesting a biopsychosocial etiology for ME/CFS or supporting management with psychotherapy or graduated activity. Tweets about the UK NICE guidelines expressed frustration with the 2007 version that recommended cognitive behavioral therapy and graded exercise therapy, and a prolonged campaign by advocacy organizations to influence subsequent versions. Tweets showed high acceptance of the 2021 UK NICE guidelines, which were seen to validate ME/CFS as a biomedical disease and recommended against graded exercise therapy. Tweets about COVID-19 often noted overlaps between post–COVID-19 condition and ME/CFS, including claims of a common biological pathway, and advised there was no cure for either condition.Conclusions:Our findings suggest research is needed to inform how best to support patients’ engagement with evidence-based care. Furthermore, while patient involvement with ME/CFS research is critical, unmanaged intellectual conflicts of interest may threaten the trustworthiness of research efforts. | |
| 610 | 4 | |a National Institute for Health & Care Excellence | |
| 651 | 4 | |a United Kingdom--UK | |
| 653 | |a Datasets | ||
| 653 | |a Application programming interface | ||
| 653 | |a Credibility | ||
| 653 | |a Memory | ||
| 653 | |a Medical diagnosis | ||
| 653 | |a Patients | ||
| 653 | |a Data mining | ||
| 653 | |a Conflicts of interest | ||
| 653 | |a Psychotherapy | ||
| 653 | |a Social networks | ||
| 653 | |a Encephalomyelitis | ||
| 653 | |a Cognitive behavioral therapy | ||
| 653 | |a Evidence based research | ||
| 653 | |a Bidirectionality | ||
| 653 | |a Cure | ||
| 653 | |a Biopsychosocial aspects | ||
| 653 | |a COVID-19 | ||
| 653 | |a Chronic fatigue syndrome | ||
| 653 | |a Health care | ||
| 653 | |a Sentiment analysis | ||
| 653 | |a Stigma | ||
| 653 | |a Mental disorders | ||
| 653 | |a Biological markers | ||
| 653 | |a Pandemics | ||
| 653 | |a Dismissal | ||
| 653 | |a Advocacy | ||
| 653 | |a Cognitive-behavioral factors | ||
| 653 | |a Medical personnel | ||
| 653 | |a Medical research | ||
| 653 | |a Qualitative research | ||
| 653 | |a Frustration | ||
| 653 | |a Etiology | ||
| 653 | |a Physicians | ||
| 653 | |a Management | ||
| 653 | |a Health care industry | ||
| 653 | |a Cognition | ||
| 653 | |a Computer mediated communication | ||
| 653 | |a Social media | ||
| 653 | |a Health services | ||
| 653 | |a Mental health services | ||
| 653 | |a Treatment methods | ||
| 653 | |a Disorders | ||
| 653 | |a Biomedicine | ||
| 653 | |a Medical treatment | ||
| 653 | |a Language processing | ||
| 653 | |a Illnesses | ||
| 700 | 1 | |a Jain, Shagun | |
| 700 | 1 | |a Gallab, Joseph | |
| 700 | 1 | |a Dharmaraj, Blossom | |
| 700 | 1 | |a Zhou, Fangwen | |
| 700 | 1 | |a Lokker, Cynthia | |
| 700 | 1 | |a Abdelkader, Wael | |
| 700 | 1 | |a Zeraatkar, Dena | |
| 700 | 1 | |a Busse, Jason W | |
| 773 | 0 | |t Journal of Medical Internet Research |g vol. 27 (2025), p. e65087 | |
| 786 | 0 | |d ProQuest |t Library Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3222368764/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3222368764/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3222368764/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |