  Community detection on social media has attracted considerable attention for
many years. However, existing methods do not reveal the relations between
communities. Communities can form alliances or engage in antagonisms due to
various factors, e.g., shared or conflicting goals and values. Uncovering such
relations can provide better insights to understand communities and the
structure of social media. According to social science findings, the attitudes
that members from different communities express towards each other are largely
shaped by their community membership. Hence, we hypothesize that
inter-community attitudes expressed among users in social media have the
potential to reflect their inter-community relations. Therefore, we first
validate this hypothesis in the context of social media. Then, inspired by the
hypothesis, we develop a framework to detect communities and their relations by
jointly modeling users' attitudes and social interactions. We present
experimental results using three real-world social media datasets to
demonstrate the efficacy of our framework.
