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Roles are a key aspect of social interactions, as they contribute to the overall predictability of social behavior (a necessary requirement to deal effectively with the people around us), and they result in stable, possibly machine-detectable behavioral patterns (a key condition for the application of machine intelligence technologies). This paper proposes an approach for the automatic recognition of roles in conversational broadcast data, in particular, news and talk shows. The approach makes use of behavioral evidence extracted from speaker turns and applies conditional random fields to infer the roles played by different individuals. The experiments are performed over a large amount of broadcast material (around 50 h), and the results show an accuracy higher than 85%.