An Efficient Fake News Detection System Using Contextualized Embeddings and Recurrent Neural Network

Authors

Junaid Ali Reshi
Rashid Ali

DOI

10.9781/ijimai.2023.02.007

Abstract

Fake news is detrimental for society and individuals. Since the information dissipation through online media is too quick, an efficient system is needed to detect and counter the propagation of fake news on social media. Many studies have been performed in last few years to detect fake news on social media. This study focusses on the efficient detection of fake news on social media, through a Natural Language Processing based approach, using deep learning. For the detection of fake news, textual data have been analyzed in unidirectional way using sequential neural networks, or in bi-directional way using transformer architectures like Bidirectional Encoder Representations from Transformers (BERT). This paper proposes ConFaDe - a deep learning based fake news detection system that utilizes contextual embeddings generated from a transformer-based model. The model uses Masked Language Modelling and Replaced Token Detection in its pre-training to capture contextual and

Source Publication

International Journal of Interactive Multimedia and Artificial Intelligence

Recommended Citation

Reshi, Junaid Ali and Ali, Rashid (2024) "An Efficient Fake News Detection System Using Contextualized Embeddings and Recurrent Neural Network," International Journal of Interactive Multimedia and Artificial Intelligence: Vol. 8: Iss. 6, Article 11.
DOI: 10.9781/ijimai.2023.02.007
Available at: https://ijimai.researchcommons.org/ijimai/vol8/iss6/11