Abstract
Most people are choosing to get their news via the internet since it is convenient and inexpensive, yet this leads to a rapid spread of fake news. Data is extremely vital in today's world, and by 2023, 120 zeta bytes of data will be released per second. Many technologies are changing the world as a result of this massive volume of data. As the Internet has become increasingly popular, people rely on online news sources to keep up with the latest developments. With the development of the usage of platforms for social media such as Instagram, Facebook and Wikipedia, the news spread rapidly to users around the world in a short period of time. This may also lead to spread of fake news that can affect the society and individuals. In this paper, we have used Machine learning (ML) in detection of fake news. With the aid of ML techniques, we seek to conduct binary categorization of various news items available online in this work. Also, we proposed a fake news detection architecture and using that we presented a comparison of different ML techniques for fake news detection.
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