A Novel Content-based Approach for Fake News Detection using Transformer Model: A Case Study of Covid-19 Dataset

According to the number of users on social media platforms and the number of followers of published content, Fake news in social media could adversely affect society and governments. In this paper, a new approach to Fake news detection is presented. Our approach is to provide a hybrid model in order to incorporate different types of features each of which is extracted by the corresponding component in the model. A transformer model generates the input of each component, namely sentiment analysis, hate speech detection, and topic modeling. Thereafter, a combination of hidden layers is performed through concatenation, which is then fed into the final classifier used for fake news prediction. We evaluated the proposed model by collecting 2500 Persian data (related to COVID-19) from the social networks complemented by various preprocessing tasks. The results demonstrate a higher accuracy (87%) for the proposed approach compared to the standard baselines.