  The evolution of electronic media is a mixed blessing. Due to the easy
access, low cost, and faster reach of the information, people search out and
devour news from online social networks. In contrast, the increasing acceptance
of social media reporting leads to the spread of fake news. This is a minacious
problem that causes disputes and endangers societal stability and harmony. Fake
news spread has gained attention from researchers due to its vicious nature.
proliferation of misinformation in all media, from the internet to cable news,
paid advertising and local news outlets, has made it essential for people to
identify the misinformation and sort through the facts. Researchers are trying
to analyze the credibility of information and curtail false information on such
platforms. Credibility is the believability of the piece of information at
hand. Analyzing the credibility of fake news is challenging due to the intent
of its creation and the polychromatic nature of the news. In this work, we
propose a model for detecting fake news. Our method investigates the content of
the news at the early stage i.e. when the news is published but is yet to be
disseminated through social media. Our work interprets the content with
automatic feature extraction and the relevance of the text pieces. In summary,
we introduce stance as one of the features along with the content of the
article and employ the pre-trained contextualized word embeddings BERT to
obtain the state-of-art results for fake news detection. The experiment
conducted on the real-world dataset indicates that our model outperforms the
previous work and enables fake news detection with an accuracy of 95.32%.
