Huge amounts of rich context social network data are generated everyday from various applications such as FaceBook and Twitter. These data involve multiple social relations which are community-driven and dynamic in nature. The complex interplay of these characteristics poses tremendous challenges on the users who try to understand the underlying patterns in the social media. We introduce an exploratory analytical framework, ContexTour, which generates visual representations for exploring multiple dimensions of community activities, including relevant topics, representative users and the community-generated content, as well as their evolutions. ContexTour consists of two novel and complementary components: (1) Dynamic Relational Clustering (DRC) that efficiently tracks the community evolution and smoothly adapts to the community changes, and (2) Dynamic Network Contour-map (DNC) that visualizes the community activities and evolutions in various dimensions. In our experiments, we demonstrate ContexTour through case studies on the DBLP dataset. The visual results capture interesting and reasonable evolution in Computer Science research communities. Quantitatively, we show 85-165X performance gain of our DRC algorithm over the baseline method. Copyright © by SIAM.