Automatic Detection of Hungarian Clickbait and Entertaining Fake News

Veronika Vincze, Martina Katalin Szabó


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.
Anthology ID:
2020.rdsm-1.6
Volume:
Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM)
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
RDSM
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
58–69
Language:
URL:
https://aclanthology.org/2020.rdsm-1.6
DOI:
Bibkey:
Cite (ACL):
Veronika Vincze and Martina Katalin Szabó. 2020. Automatic Detection of Hungarian Clickbait and Entertaining Fake News. In Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM), pages 58–69, Barcelona, Spain (Online). Association for Computational Linguistics.
Cite (Informal):
Automatic Detection of Hungarian Clickbait and Entertaining Fake News (Vincze & Szabó, RDSM 2020)
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PDF:
https://aclanthology.org/2020.rdsm-1.6.pdf