Social influence has become a field of study about how people might induce effect on others. Diffusion of information in large networks has been studied to analyze how the information flows over the network producing cascades as a main proxy of influence. For instance, microblogs such as Twitter has allowed to identify and rank influencers based on message propagation (retweets). Different factors of influence on Twitter have been identified such as: audience, interaction, users’ actions and message content. In this paper, a new web application is presented. It allows to study these factors in a temporal order based on the perspective of local influence: given a target user, who influences the user as well as who has been influenced by the user. This application is able to retrieve all retweets and favorites to filter and rank them from different perspectives based on the type of tweets and attributes such as mentions or hashtags, as well as two kind of visualizations: clusters and networks which are the outcome of user behavior by retweeting and marking as favorites.