Finding contexts of social influence in online social networks

The ever rising popularity of online social networks has not only attracted much attention from everyday users but also from academic researchers. In particular, research has been done to investigate the effect of social influence on users' actions on items in the network. However, all social influence research in the data-mining field has been done in a context-independent setting, i.e., irrespective of an item's characteristics. It would be interesting to find the specific contexts in which users influence each other in a similar manner. In this way, applications such as recommendation engines can focus on a specific context for making recommendations. In this paper, we pose the problem of finding contexts of social influence where the social influence is similar across all items in the context. We present a full-space clustering algorithm and a subspace clustering algorithm to find these contexts and test the algorithms on the Digg data set. We demonstrate that our algorithms are capable of finding meaningful contexts of influence in addition to rediscovering the predefined categories specific to the Digg news site.