Analyzing social media data having discontinuous underlying dynamics

This paper develops a real time algorithm which identifies times of emotional discontinuity as reflected in social media. The paper formulates the optimization problem to solve, develops an algorithm to solve it using dynamic programming, and illustrates the new method by analyzing mood shifts reflected in 380,000 Twitter messages related to one of the world’s most popular soccer teams, Manchester United, during their 2011–12 season.