Developing a Web-Based COVID-19 Fake News Detector With Deep Learning

Introduction

Deciphering what is fact and fiction sometimes is exhausting, particularly in the age of mass and social media. Fake news and misinformation have been spreading even faster than the novel coronavirus since the beginning of 2020. Hundreds of posts containing misguiding information on COVID-19 are being left online, along with the huge impact on individual responses and disastrous consequences to the entire social system (Barua et al., 2020). Many social and psychological impacts from misinformation, such as xenophobia, depression, violence, human rights violation for specific clusters (i.e. females, minorities, and LGBT groups), and psychological health of healthcare workers, have been becoming one of the biggest challenges during the ongoing COVID-19 pandemic (Ali et al., 2021). The ability of spreading fake news and false information has increased exponentially in the digital era as irresponsible individuals and organizations generate and share misleading posts through social media. Despite the negative aspect of hyper-connected society, information technologies can also assist to terminate using the state-of-the-art techniques. The majority of news organizations have implemented the fact-check units, which can help editors to guarantee the trustworthiness of the news source by screening and checking material in the digital manner. Third-party fact-checkers have also been incorporated to strengthen the capability of the authenticity of news content in terms of automatic assessments of news organizations, in which journalists are required to obey the Trust Principles as the bottom line. On the other hand, public authorities and lawmakers have obligations to regulate the journalism systematically by issuing related policies and rules, facilitating news literacy, and unifying government supervision, whereas monitoring fake news and misinformation is the foundation of the evidence-based policy-making process and the efficient measure of the regulation. Moreover, the supervision from any other social participants also plays a critical role in fighting against fake news and rumors. Several advanced algorithms and auto-detection systems have been applied by technology companies to identify fake news and misinformation. Individuals can also avoid fake news and rumors through comparing news and posts from a variety of online resources.