Using Big Data to Assess Legitimacy of Plastic Surgery Information on Social Media.

Abstract

The proliferation of social media in plastic surgery poses significant difficulties for the public in determining legitimacy of information. This work proposes a system based on social network analysis (SNA) to assess the legitimacy of information contributors within a plastic surgery community. The aim of this study was to quantify the centrality of individual or group accounts on plastic surgery social media by means of a model based on academic plastic surgery and a single social media outlet. To develop the model, a high-fidelity, active, and legitimate source account in academic plastic surgery (@psrc1955, Plastic Surgery Research Council) appearing only on Instagram (Facebook, Menlo Park, CA) was chosen. All follower-followed relationships were then recorded, and Gephi (https://gephi.org/) was used to compute 5 different centrality metrics for each contributor within the network. In total, 64,737 unique users and 116,439 unique follower-followed relationships were identified within the academic plastic surgery community. Among the metrics assessed, the in-degree centrality metric is the gold standard for SNA, hence this metric was designated as the centrality factor. Stratification of 1000 accounts by centrality factor demonstrated that all of the top 40 accounts were affiliated with a plastic surgery residency program, a board-certified academic plastic surgeon, a professional society, or a peer-reviewed journal. None of the accounts in the top decile belonged to a non-plastic surgeon or non-physician; however, this increased significantly beyond the 50th percentile. A data-driven approach was able to identify and successfully vet a core group of interconnected accounts within a single plastic surgery subcommunity for the purposes of determining legitimate sources of information.