This repository shares the implementation materials of a paper published by ARES'21 under the title of "FADE: Detecting Fake News Articles on the Web".
Internet-based media and social networks enable quick access to information; however, that has also made it easy to conduct disinformation campaigns. Fake news poses a serious threat to the functioning and safety of our society, as demonstrated by nation-state-sponsored campaigns to sway the 2016 US presidential election, and more recently COVID-19 pandemic hoaxes that promote false cures, putting lives at risk.
FADE is a novel approach and service that helps Internet users detect fake news. FADE discovers multiple news sources covering the same story, analyzes their reputation, and checks the trustworthiness of cited sources. Our approach does not depend on any specific social media or news source, does not rely on costly textual content analysis, and does not require lengthy offline processing. Our experiments demonstrate above 85% detection accuracy with a practical implementation. FADE offers a path to empowering the Internet community with effective tools to identify fake news.
The “code” folder contains both the server-side and client-side code for the extension. The “datasets” folder contains the training and testing data sets we used to train and test our model. Finally, the video file listed here shows how the extension works. The video can also be viewed on Youtube.