“MedEd” on Twitter: A Social Network Analysis

Abstract

Background. In the current era, Twitter is an increasingly popular tool for the dissemination of information as a social media voice. Social media is a valid, but underutilized, education tool at medical education institutions. Social media technologies provide opportunities for the presentation of information in alternative and multiple formats to enhance engagement, content creation, and motivation for individual and collaborative learning. Objective. This study examined the type of social structure and sub-clusters do exist regarding “MedEd” on the Twitter network. Additionally, it determined the top opinion leaders in these networks and which type of topics generates users’ interest regarding “MedEd”. Methods. This study applied NodeXL to analyze the results and retrieved Twitter data on November 1, 2022 by using the keywords “MedEd”. The data were saved and interpreted in the “vertices” and “edges” on the NodeXL worksheets. Results. We found that the top opinion leader (vertex) “Cryptovitas” had the highest in- betweenness and out-degree centrality. “Innov_medicine” had the in-degree centrality for networks. “In-Degree” and “Out-Degree” are the count of Tweets an opinion leader gets and forwards messages out, correspondingly. The study found that although “Cryptovitas” had the highest in-betweenness centrality, “taylorswift13” had the maximum number of followers (91,523,045) with in-betweenness centrality of 0.0. This indicates that the vertex having maximum influence with the largest number of in-betweenness centrality has not linked with several followers. Conclusions. Using Twitter embodies a potential prospect to engage the medical education community. The content of top networks’ tweets was around the number of “MedEd” innovations with the potential to significantly improve medical education delivery and innovative technologies in healthcare services. There is no link between the number of followers and in-betweenness centrality to influence the strength of social media voice. Although clinical and social tweets were there, not much was discussed regarding the curriculum reforms, continued professional development, technical issues in MedEd, and assessments. This triggers the insistence for rapid and innovative adaptations to the new learning environments and remarkable revolutions in medical education, including the encouragement of evidence-based education. The Twitter discussions promoted a research network circulating a wide range of informative innovations and collaborations.

Keywords

• meded
• twitter
• social network analysis
• medical education