Fake news detection based on news content and social contexts: a transformer-based approach.

ABSTRACT: Fake news is a real problem in today's world, and it has become more extensive and harder to identify. A major challenge in fake news detection is to detect it in the early phase. Another challenge in fake news detection is the unavailability or the shortage of labelled data for training the detection models. We propose a novel fake news detection framework that can address these challenges. Our proposed framework exploits the information from the news articles and the social contexts to detect fake news. The proposed model is based on a Transformer architecture, which has two parts: the encoder part to learn useful representations from the fake news data and the decoder part that predicts the future behaviour based on past observations. We also incorporate many features from the news cont

SUBMITTER: Raza S 

PROVIDER: S-EPMC8800852 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature