  The significance of social media has increased manifold in the past few
decades as it helps people from even the most remote corners of the world to
stay connected. With the advent of technology, digital media has become more
relevant and widely used than ever before and along with this, there has been a
resurgence in the circulation of fake news and tweets that demand immediate
attention. In this paper, we describe a novel Fake News Detection system that
automatically identifies whether a news item is "real" or "fake", as an
extension of our work in the CONSTRAINT COVID-19 Fake News Detection in English
challenge. We have used an ensemble model consisting of pre-trained models
followed by a statistical feature fusion network , along with a novel heuristic
algorithm by incorporating various attributes present in news items or tweets
like source, username handles, URL domains and authors as statistical feature.
Our proposed framework have also quantified reliable predictive uncertainty
along with proper class output confidence level for the classification task. We
have evaluated our results on the COVID-19 Fake News dataset and FakeNewsNet
dataset to show the effectiveness of the proposed algorithm on detecting fake
news in short news content as well as in news articles. We obtained a best
F1-score of 0.9892 on the COVID-19 dataset, and an F1-score of 0.9073 on the
FakeNewsNet dataset.
