  Fake news has emerged as a critical global issue, magnified by the COVID-19
pandemic, underscoring the need for effective preventive tools. Leveraging
machine learning, including deep learning techniques, offers promise in
combatting fake news. This paper goes beyond by establishing BERT as the
superior model for fake news detection and demonstrates its utility as a tool
to empower the general populace. We have implemented a browser extension,
enhanced with explainability features, enabling real-time identification of
fake news and delivering easily interpretable explanations. To achieve this, we
have employed two publicly available datasets and created seven distinct data
configurations to evaluate three prominent machine learning architectures. Our
comprehensive experiments affirm BERT's exceptional accuracy in detecting
COVID-19-related fake news. Furthermore, we have integrated an explainability
component into the BERT model and deployed it as a service through Amazon's
cloud API hosting (AWS). We have developed a browser extension that interfaces
with the API, allowing users to select and transmit data from web pages,
receiving an intelligible classification in return. This paper presents a
practical end-to-end solution, highlighting the feasibility of constructing a
holistic system for fake news detection, which can significantly benefit
society.
