In modern times, because of the the advancement of social media platforms, fake news relating to different purposes has been increasing day by day. Fake News on the internet is defined as a fabricated article with the intention to mislead, usually for profiting. Fake news and hoaxes have been there since before the advent of the Internet. Hoaxes have existed for a long time, since the "Great moon hoax" published in 1835. Along with the increase in the use of social media platforms like Facebook, Twitter etc. news spreads rapidly among millions of users within a very short span of time. This paper's purpose is to investigate the concepts, approaches and algorithms for identifying fake news articles and their creators from online social media platforms and assessing their performance. This paper introduces two models for detection of fake news. First by text classification where different classifier models were applied and it was found that RNN(LSTM) gave the best accuracy of 93 %. Second by crowd analysis where Parameter tuning method gave the best accuracy of 80 %.