Automatic Fake News Detection in Political Platforms - A Transformer-based Approach

Abstract

The dynamics and influence of fake news on Twitter during the 2020 US presidential election remains to be clarified. Here, we use a dataset related to 2020 U.S Election that consists of news articles and tweets on those articles. Therefore, it is extremely important to stop the spread of fake news before it reaches a mass level, which is a big challenge. We propose a novel fake news detection framework that can address this challenge. Our proposed framework exploits the information from news articles and social contexts to detect fake news. The proposed model is based on a Transformer architecture, which can learn useful representations from fake news data and predicts the probability of a news as being fake or real. Experimental results on real-world data show that our model can detect fake news with higher accuracy and much earlier, compared to the baselines.