The unstoppable growth that Social Networks (SN) have suffered in the last years, has produced that the data stored in those networks grows exponentially. This data appears from the information that the users provide in their corresponding profile, the different connections that they stablished while they are using the SN, and also due to the different interactions that the user perform within the SN. All this data has become in a great opportunity to extract information from the SN and also from the users. One of the most typical information that can be extracted from this data is the different groups, or clusters, of users. The main idea is to gather users in one or more groups in such a way users belonging to the same group are similar, whereas there are several differences among the users of the other groups. This problem is commonly known as Community Finding Problems, and the different groups of users are called "communities" . The data used to perform the community detection task is critical because it will affect to the quality of the communities found by the algorithms. In this way, it is possible to detect the different communities based on data extracted from the network (such as relation between users), or based on the information provided by the users in their profiles . But it is also possible to compute other metrics related to the users behaviour in order to define the different communities [2, 3, 4]. The goal of this paper is to analyse the different approaches that can be used to perform the Community Finding Tasks taking into account the different types of data available in the most popular Social Network.