Multichannel convolutional neural networks for detecting COVID-19 fake news

Abstract

Abstract By the outbreak of Coronavirus disease (COVID-19), started in late 2019, people have been exposed to false information that not only made them confused about the scientific aspects of this virus but also endangered their life. This makes fake news detection a critical issue in social media. In this article, we introduce a convolutional neural network (CNN)-based model for detecting fake news spread in social media. Considering the complexity of the fake news detection task, various features from different aspects of news articles should be captured. To this aim, we propose a multichannel CNN model that uses three distinct embedding channels: (1) contextualized text representation models; (2) static semantic word embeddings; and (3) lexical embeddings, all of which assist the classifier to detect fake news more accurately. Our experimental results on the COVID-19 fake news dataset (Patwa et al., 2020, Fighting an infodemic: COVID-19 fake news dataset, arXiv preprint arXiv:2011.03327) shows that our proposed three-channel CNN improved the performance of the single-channel CNN by 0.56 and 1.32% on the validation and test data, respectively. Moreover, we achieved superior performance compared to the state-of-the-art models in the field proposed by Shifath et al., 2021, A transformer based approach for fighting COVID-19 fake news, arXiv preprint arXiv:2101.12027 and Wani et al., 2021, Evaluating deep learning approaches for COVID-19 fake news detection, arXiv preprint arXiv:2101.04012.