ArticleJournal of health communication2018
To Retweet or Not to Retweet: Understanding What Features of Cardiovascular Tweets Influence Their Retransmission.
Article in Journal of health communication, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Effects of Social Media Narratives on Affective and Behavioral Responses to Menopause Content: Randomized Online Experimental Study.JMIR formative research · 2026Trial
- Education Research: Making a Tweetorial Fly: Features of Educational Social Media Posts Associated With High Sharing and Engagement.Neurology. Education · 2024Article
- Nutrients and Foods Recommended for Blood Pressure Control on Twitter in Japan: Content Analysis.Journal of medical Internet research · 2024Article
- Social Media Posts on Statins: What Can We Learn About Patient Experiences and Perspectives?Journal of the American Heart Association · 2024Article
- Belief-consistent information is most shared despite being the least surprising.Scientific reports · 2024Article
- A multidimensional comparative study of help-seeking messages on Weibo under different stages of COVID-19 pandemic in China.Frontiers in public health · 2024Article
- User- and Message-Level Correlates of Endorsement and Engagement for HIV-Related Messages on Twitter: Cross-sectional Study.JMIR public health and surveillance · 2022Article
- Combating Misinformation by Sharing the Truth: a Study on the Spread of Fact-Checks on Social Media.Information systems frontiers : a journal of research and innovation · 2022Article
- Examining Tweet Content and Engagement of Users With Tweets About Hikikomori in Japanese: Mixed Methods Study of Social Withdrawal.Journal of medical Internet research · 2022Article
- Interaction between official institutions and influential users of rumor control in online social networks.Frontiers in psychology · 2022Article
- The "Parallel Pandemic" in the Context of China: The Spread of Rumors and Rumor-Corrections During COVID-19 in Chinese Social Media.The American behavioral scientist · 2021Article
- Factors Driving Citizen Engagement With Government TikTok Accounts During the COVID-19 Pandemic: Model Development and Analysis.Journal of medical Internet research · 2021Article
- How Health Care Workers Wield Influence Through Twitter Hashtags: Retrospective Cross-sectional Study of the Gun Violence and COVID-19 Public Health Crises.JMIR public health and surveillance · 2021Article
- What triggers online help-seeking retransmission during the COVID-19 period? Empirical evidence from Chinese social media.PloS one · 2020Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Twitter is one of the largest social networking sites (SNSs) in the world, yet little is known about what cardiovascular health related tweets go viral and what characteristics are associated with retransmission. The current study aims to identify a function of the observable characteristics of cardiovascular tweets, including characteristics of the source, content, and style that predict the retransmission of these tweets. We identified a random sample of 1,251 tweets associated with CVD originating from the United States between 2009 and 2015. Automated coding was conducted on the affect values of the tweets as well as the presence/absence of any URL, mention of another user, question mark, exclamation mark, and hashtag. We hand-coded the tweets' novelty, utility, theme, and source. The count of retweets was positively predicted by message utility, health organization source, and mention of user handle, but negatively predicted by the presence of URL and nonhealth organization source. Regarding theme, compared to the tweets focusing on risk factor, tweets on treatment and management predicted fewer retweets while supportive tweets predicted more retweets. These findings suggest opportunities for harnessing Twitter to better disseminate cardiovascular educational and supportive information on SNSs.
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.