  Fake news detection research is still in the early stage as this is a
relatively new phenomenon in the interest raised by society. Machine learning
helps to solve complex problems and to build AI systems nowadays and especially
in those cases where we have tacit knowledge or the knowledge that is not
known. We used machine learning algorithms and for identification of fake news;
we applied three classifiers; Passive Aggressive, Na\"ive Bayes, and Support
Vector Machine. Simple classification is not completely correct in fake news
detection because classification methods are not specialized for fake news.
With the integration of machine learning and text-based processing, we can
detect fake news and build classifiers that can classify the news data. Text
classification mainly focuses on extracting various features of text and after
that incorporating those features into classification. The big challenge in
this area is the lack of an efficient way to differentiate between fake and
non-fake due to the unavailability of corpora. We applied three different
machine learning classifiers on two publicly available datasets. Experimental
analysis based on the existing dataset indicates a very encouraging and
improved performance.
