Arabic Fake News Detection: Comparative Study of Neural Networks and Transformer-Based Approaches
Fake news detection (FND) involves predicting the likelihood that a particular news article (news report, editorial, expose, etc.) is intentionally deceptive. Arabic FND started to receive more attention in the last decade, and many detection approaches demonstrated some ability to detect fake news...
Main Authors: | Maha Al-Yahya, Hend Al-Khalifa, Heyam Al-Baity, Duaa AlSaeed, Amr Essam |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi-Wiley
2021-01-01
|
Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2021/5516945 |
Similar Items
-
Detection of Hate Speech in COVID-19–Related Tweets in the Arab Region: Deep Learning and Topic Modeling Approach
by: Alshalan, Raghad, et al.
Published: (2020-12-01) -
Leveraging the Power of Deep Learning Technique for Creating an Intelligent, Context-Aware, and Adaptive M-Learning Model
by: Muhammad Adnan, et al.
Published: (2021-01-01) -
Teaching Programming to Students with Vision Impairment: Impact of Tactile Teaching Strategies on Student’s Achievements and Perceptions
by: Hind Alotaibi, et al.
Published: (2020-07-01) -
Error Detection for Arabic Text Using Neural Sequence Labeling
by: Nora Madi, et al.
Published: (2020-07-01) -
OPCNN-FAKE: Optimized Convolutional Neural Network for Fake News Detection
by: Hager Saleh, et al.
Published: (2021-01-01)