Webology, Volume 17, Number 2, December, 2020 652 http://www.webology.org A Deep Model on Hoax Detection Using Feed Forward Neural Network and LSTM Guntha Venkata Dhanush Kumar Computer Science and Engineering, Ramaiah Institute of Technology, Bangalore, India. E-mail: gunthadanush111@gmail.com Mamatha V Jadhav Assistant Professor, Computer Science and Engineering, Ramaiah Institute of Technology, Bangalore, India. E-mail: mamsdalvi@msrit.edu Anvesh Tadisetti Computer Science and Engineering, Ramaiah Institute of Technology, Bangalore, India. E-mail: anvesh4t@gmail.com Kiran Computer Science and Engineering, Ramaiah Institute of Technology, Bangalore, India. E-mail: techkiranp@gmail.com Received July 20, 2020; Accepted September 28, 2020 ISSN: 1735-188X DOI: 10.14704/WEB/V17I2/WEB17058 Abstract The topic of hoax news detection on social media has recently pulled in enormous consideration. Social media not taking any credibility for the news being spread in it makes it more difficult to contain the hoax news. The essential counter measure of comparing websites against a list of labeled hoax news sources is inflexible, and so a machine learning approach is desirable. Our project aims to use Neural Networks to detect hoax news directly, based on the text content of news articles. The model concentrates on discovering hoax news origins, based on the many articles originating from it. When a source is spotted as a maker of hoax news, we can predict with high reliability that other articles from that will similarly be hoax news. Focusing on sources augments our article mis categorization resilience, since we at that point have various facts focuses originating from each source. Keywords Neural Networks, LSTM, FFNN, RNN, Tensor Flow, Keras.