ArticleOnline journal of public health informatics2020
Understanding Discussions of Health Issues on Twitter: A Visual Analytic Study.
Article in Online journal of public health informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Investigating Online Discussions About Cancer Screening on Twitter (Subsequently Rebranded as X): Corpus Analysis.JMIR infodemiology · 2026Article
- Global Sentiment Toward Health AI at the Dawn of the ChatGPT Era: Empirical Analysis of Twitter (X) Discourse.Journal of medical Internet research · 2026Article
- Use of Large Language Models to Classify Epidemiological Characteristics in Synthetic and Real-World Social Media Posts About Conjunctivitis Outbreaks: Infodemiology Study.Journal of medical Internet research · 2025Article
- Exploring the potential of online social listening for noncommunicable disease monitoring.PeerJ · 2025Article
- Brazilian front-of-package nutrition labeling: consumer perceptions on social media platform X.Frontiers in nutrition · 2025Article
- A role for qualitative methods in researching Twitter data on a popular science article's communication.Frontiers in research metrics and analytics · 2024Article
- A Little Bird Told Me… Nutri-Score Panoramas from a Flight over Europe, Connecting Science and Society.Nutrients · 2023Article
- Analysis of Content, Social Networks, and Sentiment of Front-of-Pack Nutrition Labeling in the European Union on Twitter.Frontiers in nutrition · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Social media allows for the exploration of online discussions of health issues outside of traditional health spaces. Twitter is one of the largest social media platforms that allows users to post short comments (i.e., tweets). The unrestricted access to opinions and a large user base makes Twitter a major source for collection and quick dissemination of some health information. Health organizations, individuals, news organizations, businesses, and a host of other entities discuss health issues on Twitter. However, the enormous number of tweets presents challenges to those who seek to improve their knowledge of health issues. For instance, it is difficult to understand the overall sentiment on a health issue or the central message of the discourse. For Twitter to be an effective tool for health promotion, stakeholders need to be able to understand, analyze, and appraise health information and discussions on this platform. The purpose of this paper is to examine how a visual analytic study can provide insight into a variety of health issues on Twitter. Visual analytics enhances the understanding of data by combining computational models with interactive visualizations. Our study demonstrates how machine learning techniques and visualizations can be used to analyze and understand discussions of health issues on Twitter. In this paper, we report on the process of data collection, analysis of data, and representation of results. We present our findings and discuss the implications of this work to support the use of Twitter for health promotion.
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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.