An innovative framework for supporting social network polluting-content detection and analysis

In last years we are witnessing a growing interest in tools for analyzing big data gathered from social networks in order to find common opinions. In this context, content polluters on social networks make the opinion mining process difficult to browse valuable contents. In this paper we propose a method aimed to discriminate between pollute and real information from a semantic point of view. We exploit a combination of word embedding and deep learning techniques to categorize semantic similarities between (pollute and real) linguistic sentences. We experiment the proposed method on a data set of real-world sentences obtaining interesting results in terms of precision and recall.