Fairness and Information Access Clustering in Social Networks

Abstract

My thesis focuses on strategies to analyze fairness in information spread in social networks. Building off the field of influence maximization, I examine how the spread of information in a social network advantages some individuals over others. I review how others have handled fairness analysis in influence maximization and propose information access clustering as a new method to examine fairness. I formalize information access disparity by clustering individuals in social networks into groups based on their level of information access. I then show that these information access clusters correlate to existing measures of information access, using a coauthorship dataset as an example. I also explore variations on the information access clustering algorithm.