ArticleJMIR infodemiology
Understanding the #longCOVID and #longhaulers Conversation on Twitter: Multimethod Study.
Article in JMIR infodemiology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Twitter Analysis of Health Care Workers' Sentiment and Discourse Regarding Post-COVID-19 Condition in Children and Young People: Mixed Methods Study.Journal of medical Internet research · 2024Article
- Digital approaches in post-COVID healthcare: a systematic review of technological innovations in disease management.Biology methods & protocols · 2024Review
- Piecing together the narrative of #longcovid: an unsupervised deep learning of 1,354,889 X (formerly Twitter) posts from 2020 to 2023.Frontiers in public health · 2024Article
- The Pulse of Long COVID on Twitter: A Social Network Analysis.Archives of Iranian medicine · 2024Article
- Barriers to opioid use disorder treatment: A comparison of self-reported information from social media with barriers found in literature.Frontiers in public health · 2023Article
- State Health Department Communication about Long COVID in the United States on Facebook: Risks, Prevention, and Support.International journal of environmental research and public health · 2022Article
- Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media.JMIR infodemiologyArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe scientific community is just beginning to uncover the potential long-term effects of COVID-19, and one way to start gathering information is by examining the present discourse on the topic. The conversation about long COVID-19 on Twitter provides insight into related public perception and personal experiences.
objectiveThe aim of this study was to investigate the #longCOVID and #longhaulers conversations on Twitter by examining the combined effects of topic discussion and social network analysis for discovery on long COVID-19.
methodsA multipronged approach was used to analyze data (N=2500 records from Twitter) about long COVID-19 and from people experiencing long COVID-19. A text analysis was performed by both human coders and Netlytic, a cloud-based text and social networks analyzer. The social network analysis generated Name and Chain networks that showed connections and interactions between Twitter users.
resultsAmong the 2010 tweets about long COVID-19 and 490 tweets by COVID-19 long haulers, 30,923 and 7817 unique words were found, respectively. For both conversation types, "#longcovid" and "covid" were the most frequently mentioned words; however, through visually inspecting the data, words relevant to having long COVID-19 (ie, symptoms, fatigue, pain) were more prominent in tweets by COVID-19 long haulers. When discussing long COVID-19, the most prominent frames were "support" (1090/1931, 56.45%) and "research" (435/1931, 22.53%). In COVID-19 long haulers conversations, "symptoms" (297/483, 61.5%) and "building a community" (152/483, 31.5%) were the most prominent frames. The social network analysis revealed that for both tweets about long COVID-19 and tweets by COVID-19 long haulers, networks are highly decentralized, fragmented, and loosely connected.
conclusionsThis study provides a glimpse into the ways long COVID-19 is framed by social network users. Understanding these perspectives may help generate future patient-centered research questions.
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