ArticleJMIR medical informatics2022
Synergy Between Public and Private Health Care Organizations During COVID-19 on Twitter: Sentiment and Engagement Analysis Using Forecasting Models.
Article in JMIR medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Understanding research priorities in home health care: a topic modeling analysis of the health services literature.BMC health services research · 2026Article
- Research on New Methods of Topic Mining and Topic Prediction for Medical Preprints on Emerging Infectious Diseases.Cureus · 2025Article
- Toward Fairness, Accountability, Transparency, and Ethics in AI for Social Media and Health Care: Scoping Review.JMIR medical informatics · 2024Article
- The assessment of Twitter discourse on the new COVID-19 variant, XBB.1.5, through social network analysis.Vaccine: X · 2023Article
- Crowdsourced Perceptions of Human Behavior to Improve Computational Forecasts of US National Incident Cases of COVID-19: Survey Study.JMIR public health and surveillance · 2022Article
- Resilience of political leaders and healthcare organizations during COVID-19.PeerJ. Computer science · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSocial media platforms (SMPs) are frequently used by various pharmaceutical companies, public health agencies, and nongovernment organizations (NGOs) for communicating health concerns, new advancements, and potential outbreaks. Although the benefits of using them as a tool have been extensively discussed, the online activity of various health care organizations on SMPs during COVID-19 in terms of engagement and sentiment forecasting has not been thoroughly investigated.
objectiveThe purpose of this research is to analyze the nature of information shared on Twitter, understand the public engagement generated on it, and forecast the sentiment score for various organizations.
methodsData were collected from the Twitter handles of 5 pharmaceutical companies, 10 US and Canadian public health agencies, and the World Health Organization (WHO) from January 1, 2017, to December 31, 2021. A total of 181,469 tweets were divided into 2 phases for the analysis, before COVID-19 and during COVID-19, based on the confirmation of the first COVID-19 community transmission case in North America on February 26, 2020. We conducted content analysis to generate health-related topics using natural language processing (NLP)-based topic-modeling techniques, analyzed public engagement on Twitter, and performed sentiment forecasting using 16 univariate moving-average and machine learning (ML) models to understand the correlation between public opinion and tweet contents.
resultsWe utilized the topics modeled from the tweets authored by the health care organizations chosen for our analysis using nonnegative matrix factorization (NMF): c
conclusionsOur findings indicate that people engage more on topics such as COVID-19 than medical trials and customer experience. In addition, there are notable differences in the user engagement levels across organizations. Global organizations, such as WHO, show wide variations in engagement levels over time. The sentiment forecasting method discussed presents a way for organizations to structure their future content to ensure maximum user engagement.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.