Evolving HMM for ranking Twitter influence

Identifying influence of users in a Twitter network has been researched from quite some time. Many researchers have proposed different models for calculating influence of a particular user in a Twitter network. The motivation has been to target such users for digital marketing or to solicit users who might be performing terrorist activities. The static influence of user has been captured through topology based methods and temporal influence is captured through HMM model. In this research an evolutionary based HMM model for capturing the temporal influence of a Twitter user has been proposed. The reason is Baum Welch algorithm normally used to determine the emission and transition probabilities may converge to a local optimum point. Evolutionary algorithms search random portions of entire solution space and the probability of finding global optima increases.