A statistical analysis and modeling of information diffusion across online social networks

In recent years, online social networks have become a very popular and effective forum for information exchange. These large, highly interconnected networks span the globe and have the ability to disseminate information in a fraction of the time it would take other communication networks. Given the myriad ways in which online social networks can be used, creating accurate, predictive models for the spread of information across them is very valuable. With that, modeling processes on large networks is a difficult task. It is computationally expensive, and usually prohibitive, to model a process on the entirety of a very large network. Given these complexities, creating smaller network graphs that are characteristically similar to the original networks graphs enable researchers to run models that are otherwise not feasible. This project aims to create prototypic networks and model the spread of information across them using traditional and network-based epidemiological models to better understand how information spreads across an online social network. More specifically, the focus will be on the spread of the news of a scientific discovery, i.e., the Higgs-Boson particle, on Twitter