Quantifying Emerging Data Capital: An Experiment in Social Media Clout


Tang C. (first author). In Preparation. “Quantifying Emerging Data Capital: An Experiment in Social Media Clout.” Proceedings of the National Academy of Sciences of the United States of America.


Network analytics using a force-directed graph drawing algorithm can offer a variety of intuitive processes to understand social gravity. We assume that such gravity is influenced by social media clout, representing interpersonal relationships expressed by language response. While immediate or delayed language responses may define a distant or close relationship between two individuals, this research only considers the evolution of the topics discussed in in-group members (i.e., they are alike) for data privacy concerns. We first utilize a dynamic Latent Dirichlet Allocation model to extract a specified number of topics from two Facebook™ pages (10 topics for each) between October 1, 2016, and September 30, 2018. One page lists a closed group with 18,946 members (as of the same time) created on December 3, 2006; the other has an open group with 11,999 members (as of September 2018) built on May 10, 2008. A total of 3,952 people participates in overlapping. Next, we present techniques for using social gravity as force-directed layouts to produce drawings of complex networks for these topics. The force-directed graphs are an intuitive representation of each topic’s origin and evolution in posts and inter-human relations among the post categories to demonstrate real-time topic intensity. We then evaluate the result networks with the Ramsey Theorem. The two public and private venture-capital groups’ findings provide direct evidence of exploring data capital opportunities offered by social media clout.



Last updated on 02/10/2021