COVID-19 Fake News Detection Using Ensemble-Based Deep Learning Model

摘要

Fake news on various medicines, foods, and vaccinations relating to the COVID-19 pandemic has increased dramatically. These fake news reports lead individuals to believe in false and sometimes harmful claims and stories, and they also influence people’s vaccination opinions. Immediately detecting COVID-19 false news can help to reduce the spread of fear, confusion, and potential health risks among citizens. An ensemble-based deep learning model for detecting COVID-19-related fake news on Twitter is proposed in this article. CT-BERT, BERTweet, and roberta are three different models that are fine-tuned on COVID-19-linked text data to separate fake and authentic news. In addition, the proposed ensemble-based model is compared to a variety of standard machine learning and deep learning models. In the detection of COVID-19 fake news from Twitter, the proposed ensemble-based deep learning model achieved state-of-the-art performance with a weighted $F_1$F1-score of 0.99.