  Recently, news consumption using online news portals has increased
exponentially due to several reasons, such as low cost and easy accessibility.
However, such online platforms inadvertently also become the cause of spreading
false information across the web. They are being misused quite frequently as a
medium to disseminate misinformation and hoaxes. Such malpractices call for a
robust automatic fake news detection system that can keep us at bay from such
misinformation and hoaxes. We propose a robust yet simple fake news detection
system, leveraging the tools for paraphrasing, grammar-checking, and
word-embedding. In this paper, we try to the potential of these tools in
jointly unearthing the authenticity of a news article. Notably, we leverage
Spinbot (for paraphrasing), Grammarly (for grammar-checking), and GloVe (for
word-embedding) tools for this purpose. Using these tools, we were able to
extract novel features that could yield state-of-the-art results on the Fake
News AMT dataset and comparable results on Celebrity datasets when combined
with some of the essential features. More importantly, the proposed method is
found to be more robust empirically than the existing ones, as revealed in our
cross-domain analysis and multi-domain analysis.
