ArticleBMJ health & care informatics2020
What people share about the COVID-19 outbreak on Twitter? An exploratory analysis.
Article in BMJ health & care informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
7 citing papers in PubMed.
- COVID-19 Public Health Communication on X (Formerly Twitter): Cross-Sectional Study of Message Type, Sentiment, and Source.JMIR formative research · 2025Article
- People's Attitude and Perception of the Pandemic on Twitter: A Case Study of COVID-19 in India.Cureus · 2024Article
- Reporting of Ethical Considerations in Qualitative Research Utilizing Social Media Data on Public Health Care: Scoping Review.Journal of medical Internet research · 2024Article
- Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review.Journal of medical Internet research · 2023Article
- "Help Us!": a content analysis of COVID-19 help-seeking posts on Weibo during the first lockdown.BMC public health · 2023Article
- Use of the Hashtag #DataSavesLives on Twitter: Exploratory and Thematic Analysis.Journal of medical Internet research · 2022Article
- The Spread of COVID-19 Crisis Communication by German Public Authorities and Experts on Twitter: Quantitative Content Analysis.JMIR public health and surveillance · 2021Article
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
backgroundThe recent outbreak of respiratory illness caused by COVID-19 in Wuhan, China, has received global attention as it has infected thousands of individuals there, and later it has also been reported from other countries internationally. This study aims at performing an exploratory study on Twitter to understand the information shared among the community regarding the COVID-19 outbreak.
methodsCOVID-19 related tweets were collected from Twitter using keywords from 18 January to 25 January 2020. Top-ranking tweets were taken as samples and then categorised based on the content. Expressions or opinion tweets were analysed qualitatively to understand the mindset of the people regarding the outbreak. Theme wise reachability evaluation of the messages was also performed.
resultsBased on the content of the tweets, five themes were evolved: (1) general information; (2) health information; (3) expressions; (4) humour and (5) others. 57.42% of messages are general information followed by expressive tweets (24.12%). Humorous messages were liked the most, whereas health information tweets were retweeted the maximum. Fear was the predominant emotion expressed in the messages.
conclusionThe results of the study would be useful to focus on the dissemination of the right information and effective communication on Twitter related to health and outbreak management.
Indexed as
Identifiers
What OpenQuestion holds
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.