@inproceedings{vincze-szabo-2020-automatic,
    title = "Automatic Detection of {H}ungarian Clickbait and Entertaining Fake News",
    author = "Vincze, Veronika  and
      Szab{\'o}, Martina Katalin",
    editor = "Aker, Ahmet  and
      Zubiaga, Arkaitz",
    booktitle = "Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM)",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.rdsm-1.6/",
    pages = "58--69",
    abstract = "Online news do not always come from reliable sources and they are not always even realistic. The constantly growing number of online textual data has raised the need for detecting deception and bias in texts from different domains recently. In this paper, we identify different types of unrealistic news (clickbait and fake news written for entertainment purposes) written in Hungarian on the basis of a rich feature set and with the help of machine learning methods. Our tool achieves competitive scores: it is able to classify clickbait, fake news written for entertainment purposes and real news with an accuracy of over 80{\%}. It is also highlighted that morphological features perform the best in this classification task."
}