Information Diffusion on Social Media Platforms

In the last two decades, more and more social activities and transactions are moving online, making social media one of the major channels for information diffusion and significantly shaping people's interaction patterns and communication outcomes. This dissertation utilizes social network analysis (SNA) and data analytics techniques to study how information diffusion occurs on social media platforms and consists of two essays. The first essay develops a data analytics-based systematic literature review (SLR) protocol to examine the current state of social media-related studies on information diffusion. The protocol incorporates scraping tools to collect articles from seven bibliographic databases, text analytics, SNA, natural language processing, citation analysis, and main path analysis to analyze a large number of articles. The second essay builds on the results derived from the first essay to investigate the diffusion patterns and outcomes of popular movie discussions on Twitter through two SNA theories (the strength of weak ties theory and the complex contagion theory). Using a large data set collected from Twitter, a longitudinal information diffusion model is constructed to test the effects of the contagion characteristics on the four dimensions of the information diffusion process (speed, scope, intensity, and duration) and the associated movies market performance. The dissertation is among the first to empirically test the propositions of the two theories on a social media platform using actual user data, thereby contributing to enhancing the practical contributions of the two theories. Future researchers and industry practitioners can utilize the results of the study to investigate ways to facilitate effective communications on social media, expand the scope and depth of information dissemination, reach a broader audience, and improve marketing and advertising strategies.