The rapid evolution of modern social networks motivates the design of networks based on users’ interests. Using popular social media such as Facebook and Twitter, the authors show that this new perspective can generate more meaningful information about the networks. In this paper, they model user-interest based networks by deducing intent from social media activities such as comments and tweets of millions of users in Facebook and Twitter, respectively. This interactive content derives networks that are dynamic in nature as the user interests can evolve due to temporal and spatial activities occurring around the user. To understand and analyze these networks, they develop a new approach for mining communities to overcome the limitations of the widely used Clauset, Newman, and Moore (CNM) community detection algorithm.