This paper presents an automatic approach to analyze the human interactions appearing in multiparty data, aiming at understanding the data content and at extracting social informa- tion such as Which role do people play?, What is their attitude?, or Can people be split into meaningful groups?. To extract such information, we use a set of mathematical techniques, namely Social Networks Analysis (SNA), developed by sociologists to analyze social interac- tions. This paper shows that a strong connection can be established between the content of broadcast data and the social interactions of the individuals involved in the recordings. Experiments aiming at assigning each individual to a social group corresponding to a specific topic in broadcast news, and experiments aiming at recognizing the role played by each indi- vidual in multiparty data are presented in this paper. The results achieved are satisfactory, which suggests on one side that the application of SNA to similar problems could lead to useful contributions in the domain of multimedia content analysis, and on the other side, that the presented analysis of social interactions could be a significant breakthrough for affective computing.