This research thesis is aiming to exploit valuable social media, especially those available in online communities of social network sites, in order to perform social studies about healthcare issues. Based on a practical approach, a process was defined to conduct such studies. This process relied on tailored computational tools to provide support for specific tasks such as contente retrieval, selection, and analysis. Two tools that stand out are presented because of their utility and the complexity of the process in which their development was based on. The first tool, for the benefit of online community analysis, is the Community Association Map, a process developed to support experts in understanding users’ interests based on their associations within their communities. Our second tool (TorchSR) aims to aid analysts in the selection of discussions from online forums to be manually analyzed by (qualitative) research techniques (e.g. content and discourse analysis). This task, which was defined as solving the content selection problem, was tackled with a tool based on unsupervised machine learning techniques, such as hierarchical clustering. An exploratory study case shows that TorchSR helps analysts in dealing with the problem. The proposed process was employed in two studies about relevant healthcare issues (i.e. hepatitis C and drug abuse) which resulted in interesting findings in the field of public health. In conclusion, this thesis presents a practical application of computational social science to the field of health, through development of a process and tools used to support analysts and improve its application.