The topic of fake and satire news has drawn attention from both the public and the academic communities. Such misinformation has the potential for extremely negative impacts on individuals and society. Automatic fake and satire news detection is a challenging problem in deception detection, and it has tremendous real-word political and social impacts. In this paper, we contribute a useful French satire dataset to the research community and provide a satire news detection system by using machine learning for significantly automating classifications. In addition we present the preliminary results of our research work in order to identify real news from satire stories, thus ultimately reduce fake and satire news spreads.