TY  - CONF
T1  - Detection of Satiric News on Social Media: Analysis of the Phenomenon with a French Dataset
A1  - Liu, Zhan
A1  - Shabani, Shaban
A1  - Glassey Balet, Nicole
A1  - Maria, Sokhn
TI  - The 28th International Conference on Computer Communications and Networks (ICCCN 2019)
Y1  - 2019
PB  - IEEE
CY  - Valencia, Spain
SN  - 978-1-7281-1856-7
SN  - 2637-9430
UR  - https://ieeexplore.ieee.org/document/8847041
M2  - doi: 10.1109/ICCCN.2019.8847041
KW  - Classification
KW  - fake news
KW  - French dataset
KW  - machine learning
KW  - media
KW  - news satire
KW  - social media
N2  - 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.
ER  -