Multilingual Fake News Detection with Satire - HAL-SHS - Sciences de l'Homme et de la Société Access content directly
Conference Papers Year :

Multilingual Fake News Detection with Satire

Gaël Guibon
  • Function : Author
  • PersonId : 1032238
  • IdHAL : gael-guibon
Liana Ermakova
Anton Firsov
  • Function : Author
  • PersonId : 1032166

Abstract

The information spread through the Web influences politics, stock markets, public health, people's reputation and brands. For these reasons, it is crucial to filter out false information. In this paper, we compare different automatic approaches for fake news detection based on statistical text analysis on the vaccination fake news dataset provided by the Storyzy company. Our CNN works better for discrimination of the larger classes (fake vs trusted) while the gradient boosting decision tree with feature stacking approach obtained better results for satire detection. We contribute by showing that efficient satire detection can be achieved using merged embeddings and a specific model, at the cost of larger classes. We also contribute by merging redundant information on purpose in order to better predict satire news from fake news and trusted news.
Fichier principal
Vignette du fichier
Multilingual_Fake_News_Detection_with_Satire_on_Vaccination_Topic.pdf (2.34 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

halshs-02391141 , version 1 (03-12-2019)

Identifiers

  • HAL Id : halshs-02391141 , version 1

Cite

Gaël Guibon, Liana Ermakova, Hosni Seffih, Anton Firsov, Guillaume Le Noé-Bienvenu. Multilingual Fake News Detection with Satire. CICLing: International Conference on Computational Linguistics and Intelligent Text Processing, Apr 2019, La Rochelle, France. ⟨halshs-02391141⟩
655 View
673 Download

Share

Gmail Facebook Twitter LinkedIn More