Distributed Graph Techniques to Quantify Social Media Engagement of Covid-19 Scientific Literature through Incremental Tweet Chain Measurements

Abstract

This research combined existing distributed data and graph techniques with a novel approach, refined over many experiments, to quantify social media engagement. The developed methodology scores engagement through measures including unique users reached, number of coverage items about which the user posted, and number and length of social post chains. In calculating scores, the use of quality measures goes beyond page rank's more limited concern with the centrality of a user within the social reference graph structure. The experiments used data comprised of scientific literature from the CORD-19 dataset, Scopus citation graphs relating to CORD-19, and PlumX altmetrics with corresponding Twitter posts intersecting with papers in the CORD-19 and Scopus citation graphs. The most significant outcomes of the research are two scoring algorithms, Social Media Engagement (SME) and Social Media Noise (SMN), which quantify complexity of engagement, or lack thereof, by users within social data. The incremental design allows new data to be added to a cumulative score without recalculation. SME and SMN algorithms have wide applicability for all social data at any scale or velocity and could become the basis for a new class of altmetrics.