• Nerissa Pereira
• Simran Dabreo
• Linnet Rodrigues
• Prof. Merly Thomas

In recent times there has been a tremendous increase in the spread of false information. The issues related to the spread of fake news across multiple social media platforms as well as random web pages from the internet has gained massive importance from the modern day journalism due to its emerging popularity in various research communities. The intention behind designing and spreading the fake news content is to misguide the readers and make them believe the false news. In our daily lives, it is a difficult task to distinguish the fake content from the news articles; as most of the fake articles tend to be perfectly structured to make the readers believe their content. Hence fake news cannot be classified solely based on the content, but we also need to consider multiple attributes such as the source of the news, the user engagements, the authenticity of the user sharing the news, etc. In this paper we have come up with the applications of NLP and Neural Networks techniques for detecting the 'fake news'. Generating a model based only on a count vectorizer (using word tallies) or a (Term Frequency Inverse Document Frequency) tfidf matrix, (word tallies relative to how often they’re used in other articles in your dataset) we can only get a certain accuracy; as they will not consider the interdependency between a variety of features present in the news content; as well as the order of the content. For this, we propose our system using a deep learning unit called as LSTM combined with neural networks and try generating the comparative analysis.

Social Media, Twitter, NLP, Neural Networks, Deep Learning Models, RNN, LSTM

"Comparative Analysis of Fake News Detection using Machine Learning and Deep Learning Techniques.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 4, page no.1379-1385, April-2020, Available :http://www.jetir.org/papers/JETIR2004387.pdf

"Comparative Analysis of Fake News Detection using Machine Learning and Deep Learning Techniques.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 4, page no. pp1379-1385, April-2020, Available at : http://www.jetir.org/papers/JETIR2004387.pdf