Résumé
The topic of deceptive and satiric news has drawn
attention from both the public and the academic community, as
such misinformation has the potential to have extremely adverse
effects on individuals and society. Detecting false and satiric news
automatically is a challenging problem in deception detection,
and it has tremendous real-word political and social influences.
In this paper, we contribute a useful French satiric dataset to the
research community and provide a satiric news detection system
using machine learning to automate classifications significantly.
In addition, we present the preliminary results of our research
designed to discriminate real news from satiric stories, and thus
ultimately reduce false and satiric news distribution.