Since their beginnings, social networks have affected the way people communicate and interact with each other. The continuous growing and pervasive use of social media offers interesting research opportunities for analysing the behaviour and interactions of users. Nowadays, interactions are not only limited to social relations, but also to reading and writing activities. Thus, multiple and complementary information sources are available for characterising users and their activities. One task that could benefit from the integration of those multiple sources is community detection. However, most techniques disregard the effect of information aggregation and continue to focus only on one aspect: the topological structure of networks. This paper focuses on how to integrate social and content-based information originated in social networks for improving the quality of the detected communities. A technique for integrating both the multiple information sources and the semantics conveyed by asymmetric relations is proposed and extensively evaluated on two real-world datasets. Experimental evaluation confirmed the differentiated impact that each information source has on the quality of the detected communities, and shed some light on how to improve such quality by combining both social and content-based information.