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This paper describes an approach of information retrieval which takes into account social signals associated with Web resources to estimate its relevance to a query. We show how these data, which are in the form of actions within social activities (e.g. like, tweet), can be exploited to quantify social properties such as popularity and reputation. We propose a model that combines the social relevance, estimated from these properties, with the conventional textual relevance. We evaluated the effectiveness of our approach on IMDb dataset containing 32706 resources and their social characteristics collected from several social networks. We used also the selected criteria to learn models to determine their effectiveness in information retrieval. Our experimental results are promising and show the interest of integrating social signals in retrieval model to enhance a search.