The massive adoption of social networks increased the need to analyze users’ data and interactions to detect and block the spread of propaganda and harassment behaviors, as well as to prevent actions influencing people towards illegal or immoral activities. In this paper, we propose HURI, a method for social network analysis that accurately classifies users as safe or risky, according to their behavior in the social network. Specifically, the proposed hybrid approach leverages both the topology of the network of interactions and the semantics of the content shared by users, leading to an accurate classification also in the presence of noisy data, such as users who may appear to be risky due to the topic of their posts, but are actually safe according to their relationships. The strength of the proposed approach relies on the full and simultaneous exploitation of both aspects, giving each of them equal consideration during the combination phase. This characteristic makes HURI different from other approaches that fully consider only a single aspect and graft partial or superficial elements of the other into the first. The achieved performance in the analysis of a real-world Twitter dataset shows that the proposed method offers competitive performance with respect to eight state-of-the-art approaches.