Fake news detection with deep learning and machine learning methods

In our age, especially since the rise of technology and social media, fake news has become a problem in society. News has had an impact on society throughout history. Today, when online platforms are one of the biggest communication tools, we access the news over the internet. When we cannot access the right source of information, serious problems arise for the life of society. Anymore we see fake news spreading more and faster in some situations than real information. The availability of technology has brought us many of its problems too alongside the advantages of it. Fake news has been used for directing society and for social engineering rather than an innocent information mistake. Today, we are faced with a false fraud that guides social movements, makes wrong decisions, and affects political and economic events. The events experienced show us that work such as big data processing and social media data analysis needs to be done. In this study, fake news data is used which is a more general topic than fake fabricated or fictional content that can change this economy, sociology, politics. I evaluate this different artificial intelligence method, which aims at using machine learning and deep learning methods of fake news, with fake news detection. I compare NLP, LSTMs, and traditional machine method learning. LSTMs has had higher classification success in most machine method learning methods. The fact that a lot of work has to be done related to big data processing, internet news analyzing, social media data analysis encouraged me to this field. This thesis is expected to be useful for producing more effective solutions to fake news, which is an important problem.