To Retweet or Not to Retweet: Understanding What Features of Cardiovascular Tweets Influence Their Retransmission

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.

Acknowledgments

The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. We also thank Elizabeth Asch and Maramawit Abera for their great help with coding the cardiovascular tweets.

Notes

1. A full list of tweets under each theme are available upon request.

2. Given that the number of retweets could also be influenced by the number of followers of the tweeter, we fitted an alternative model with the current predictors and also controlling for the number of followers. The natural log transformation was performed on the number of followers, which was originally zero-inflated and highly skewed, to make the data normally distributed. Most of the results obtained by the alternative model remained similar with the model reported in the main text, with only the treatment and management theme being nonsignificant in the alternative model.

Additional information

Funding

Research reported in this manuscript was supported by the National Institutes of Health (NIH) under award number 5R01HL122457-02.