Detection of satiric news on social media: analysis of the phenomenon with a French dataset

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

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.


Keywords:
Conference Type:
full paper
Faculty:
Economie et Services
School:
HEG Arc
HEG-VS
Institute:
Institut du Management et des Systèmes d'Information
Institut Informatique de gestion
Subject(s):
Informatique
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 Communication and Networks (ICCCN) 219
DOI:
ISBN:
978-1-7281-1856-7
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2019-10-21, last modified 2019-10-22

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