Deep contextualized text representation and learning for fake news detection

In recent years, due to the widespread use of social media and broadcasting agencies around the world, people are extremely exposed to being affected by false information and fake news, all of which have negative impacts on both collective thoughts and governments' policies. In recent years, the great success of pre-trained models for embedding contextual information from texts motivates researchers to utilize these embeddings in different natural language processing tasks. However, in a complex task like fake news detection, it is not determined which contextualized embedding can assist the classifier with more valuable features. Due to the lack of a comparative study about utilizing different contextualized pre-trained models besides distinct neural classifiers, we aim to dive into a comparative study about using different classifiers and embedding models. In this paper, we propose three classifiers with different pretrained models for embedding input news articles. We connect Single-Layer Perceptron (SLP), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN) after the embedding layer which consists of novel pre-trained models such as BERT, RoBERTa, GPT2, and Funnel Transformer in order to benefit from deep contextualized representation provided by those models as well as deep neural classifications. We evaluate our proposed models on three wellknown fake news datasets: LIAR (Wang, 2017), ISOT (Ahmed et al., 2017), and COVID-19 Patwa et al. (2020). The results on these three datasets show the superiority of our proposed models for fake news detection compared to the state-of-the-art models. The results show 7% and 0.1% improvements in classification accuracy compared to the proposed model by Goldani et al. (2021) on LIAR and ISOT, respectively. We also achieved 1% improvement compared to the proposed model by Shifath et al. (2021) on the COVID-19 dataset.