The social Web, represented mainly by social media sites, is characterized by enriching the life and activities of its users, thus giving rise to new forms of communication and interaction. The unlimited possibilities offered by social media sites generate new problems related to information overload, the quality of published information and the formation of new social relationships. This opens the possibility to the contamination of social media with unwanted and unreliable content (false news, rumours, spam, hoaxes), which influences the perception and understanding of events, exposing users to risks. Motivated by the large amount of heterogeneous information available on the social Web and considering the consequences of the exposure to unwanted and unreliable content on social media, the existence of accounts dedicated to sharing said content, and the rapid dispersion of both phenomena, the goal of this work is is to define a profile to describe and estimate the trustworthiness or reputation of users, to avoid making “bad” recommendations that could favour the propagation of unreliable content and polluting users. The contribution of this work lies in the provision of reliable recommendation systems based on the integration of techniques that automatically allow the detection of unreliable content and the users publishing it. The final aim is to reduce the negative effects of the existence and propagation of such content, and thus improving the quality of the recommendations.