This thesis studies the universe of Online Social Networks (OSNs) and their influencers, i.e., popular users, by applying instruments that typically belongs to the financial fields, technical analysis in particular. Two aspects of OSNs have been investigated. The first is the correlation between social network dynamics (e.g. fanbase evolution) and other exogenous dynamics (e.g. search engines queries). The second is the synergy between couples of influencers, meant as the highly correlated movement of dynamically normalized social network metrics of two influencers during a variable length time interval. First, this work provides a basic understanding of the fundamental financial concepts on top of which our reasoning is built.