Diffusing science through social networks: The case of breastfeeding communication on Twitter.

• Main Authors: Sara Moukarzel, Martin Rehm, Miguel Del Fresno, Alan J Daly
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2020-01-01
• Series: PLoS ONE
• Online Access: https://doi.org/10.1371/journal.pone.0237471

Breastfeeding is one of many health practices known to support the survival and health of mother and infant, yet low breastfeeding rates persist globally. These rates may be influenced by limited diffusion of evidence-based research and guidelines from the scientific community (SC). As recently highlighted by the National Academy of Sciences, there is a need for the SC to diffuse its findings to the public more effectively online, as means to counteract the spread of misinformation. In response to this call, we gathered data from Twitter for one month from major breastfeeding hashtags resulting in an interconnected social network (n = 3,798 users). We then identified 59 influencers who disproportionately influenced information flow using social network analysis. These influencers were from the SC (e.g. academics, researchers, health care practitioners), as well as interested citizens (IC) and companies. We then conducted an ego-network analysis of influencer networks, developed ego maps, and compared diffusion metrics across the SC, IC and company influencers. We also qualitatively analyzed their tweets (n = 711) to understand the type of information being diffused. SC influencers were the least efficient communicators. Although having the highest tweeting activity (80% of tweets), they did not reach more individuals compared to IC and companies (two-step ego size: 220± 99, 188 ± 124, 169 ± 97 respectively, P = 0.28). Content analysis of tweets suggest IC are more active than the SC in diffusing evidence-based breastfeeding knowledge, with 35% of their tweets around recent research findings compared to only 12% by the SC. Nonetheless, in terms of outreach to the general public, the two-step networks of SC influences were more heterogenous than ICs (55.7 ± 5.07, 50.9 ± 12.0, respectively, P<0.001). Collectively, these findings suggest SC influencers may possess latent potential to diffuse research and evidence- based practices. However, the research suggests specific ways to enhance diffusion.