Detection of news satire on social media : case study of French dataset and analysis

Shabani, Shaban (Haute école de gestion Arc, HES-SO // Haute Ecole Spécialisée de Suisse Occidentale) ; Sokhn, Maria (Haute école de gestion Arc, HES-SO // Haute Ecole Spécialisée de Suisse Occidentale) ; Liu, Zhan (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Glassey Balet, Nicole (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis))

The topic of fake and satire news has drawn attention from both the public and the academic communities. Such misinformation has the potential for extremely negative impacts on individuals and society. Automatic fake and satire news detection is a challenging problem in deception detection, and it has tremendous real-word political and social impacts. In this paper, we contribute a useful French satire dataset to the research community and provide a satire news detection system by using machine learning for significantly automating classifications. In addition we present the preliminary results of our research work in order to identify real news from satire stories, thus ultimately reduce fake and satire news spreads.


Keywords:
Conference Type:
full paper
Faculty:
Economie et Services
School:
HEG Arc
Institute:
IDO - Institut de Digitalisation des organisations
Institut du Management et des Systèmes d'Information
Subject(s):
Economie/gestion
Publisher:
Valencia, Spain, 29 July - 1 August 2019
Date:
2019-07
Valencia, Spain
29 July - 1 August 2019
Pagination:
6 p.
Published in:
Proceedings of the 28th International conference on computer communications and networks (ICCCN 2019)
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2019-09-20, last modified 2019-10-31

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