Abstract— Because it's quick to access, affordable, and appealing, people are increasingly turning to social media for news. It's also capable of spreading "false news." Fake news' massive distribution has left an indelible mark on people and culture. Some people publish false information on social media in order to obtain attention, money, or political power. We need to improve our ability to distinguish between phoney and authentic news. The one-of-a-kind feature of detecting bogus news on social media that renders present detection algorithms ineffective or unsuitable. After then, it's critical to think about secondary data. Secondary information could include a user's social media activities. So, using the K-Nearest Neighbor classifier, we describe a straightforward method for detecting bogus news on social media in this research paper. This model had a classification accuracy of about 79 percent when evaluated against the Facebook news posts dataset.Keywords— Fake news; K-Nearest Neighbor; Data Mining; Supervised.