Social Network Polluting Contents Detection through Deep Learning Techniques

Nowadays social networks are widespread used not only to enable users to share comments with other users but also as tool from which is possible to extract knowledge. As a matter of fact, social networks are increasingly considered to understand the opinion trend about a politician or related to a certain event that occurred: in general social networks have been proved useful to understand the public opinion from both governments and companies. In addition, also from the end users point of view it is difficult to identify real contents. This is the reason why 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 dataset composed of real-world sentences gathered from the Twitter social network obtaining interesting results in terms of precision and recall.