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@INPROCEEDINGS{,
     author = {Liu, Zhan and Shabani, Shaban and Glassey Balet, Nicole and Maria, Sokhn},
   keywords = {Classification, fake news, French dataset, machine learning, media, news satire, social media},
      month = jul,
      title = {Detection of Satiric News on Social Media: Analysis of the Phenomenon with a French Dataset},
  booktitle = {The 28th International Conference on Computer Communications and Networks (ICCCN 2019)},
       year = {2019},
  publisher = {IEEE},
   location = {Valencia, Spain},
       issn = {2637-9430},
       isbn = {978-1-7281-1856-7},
        url = {https://ieeexplore.ieee.org/document/8847041},
        doi = {10.1109/ICCCN.2019.8847041},
   abstract = {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.}
}