Building a community of practice through social media using the hashtag #neoEBM

• Published in: PloS one 2021-05, Vol.16 (5), p.e0252472-e0252472
• Main Authors: Keir, Amy, Bamat, Nicolas, Hennebry, Bron, King, Brian, Patel, Ravi, Wright, Clyde, Scrivens, Alexandra, ElKhateeb, Omar, Mitra, Souvik, Roland, Damian
• Format: Article
• Language: English
• Subjects: Children; Community-based research; Computer and Information Sciences; Data analysis; Earth Sciences; Editing; Emergency medical care; Emergency medical services; Emergency vehicles; Epidemiology; Evaluation; Hospitals; Influence; Learned societies; Medical research; Medicine; Medicine and Health Sciences; Methodology; Newborn babies; Pediatrics; Public health; Retrieval; Reviews; Sapphire; Social media; Social networks; Social Sciences; Tagging
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0252472

Social media use is associated with developing communities of practice that promote the rapid exchange of information across traditional institutional and geographical boundaries faster than previously possible. We aimed to describe and share our experience using #neoEBM (Neonatal Evidence Based Medicine) hashtag to organise and build a digital community of neonatal care practice. Analysis of #neoEBM Twitter data in the Symplur Signals database between 1 May 2018 to 9 January 2021. Data on tweets containing the #neoEBM hashtag were analysed using online analytical tools, including the total number of tweets and user engagement. Since its registration, a total of 3 228 distinct individual Twitter users used the hashtag with 23 939 tweets and 37 259 710 impressions generated. The two days with the greatest number of tweets containing #neoEBM were 8 May 2018 (n = 218) and 28 April 2019 (n = 340), coinciding with the annual Pediatric Academic Societies meeting. The majority of Twitter users made one tweet using #neoEBM (n = 1078), followed by two tweets (n = 411) and more than 10 tweets (n = 347). The number of individual impressions (views) of tweets containing #neoEBM was 37 259 710. Of the 23 939 tweets using #neoEBM, 17 817 (74%) were retweeted (shared), 15 643 (65%) included at least one link and 1 196 (5%) had at least one reply. As #neoEBM users increased over time, so did tweets containing #neoEBM, with each additional user of the hashtag associated with a mean increase in 7.8 (95% CI 7.7-8.0) tweets containing #neoEBM. Our findings support the observation that the #neoEBM community possesses many of the characteristics of a community of practice, and it may be an effective tool to disseminate research findings. By sharing our experiences, we hope to encourage others to engage with or build online digital communities of practice to share knowledge and build collaborative networks across disciplines, institutions and countries.