Detection of Faux News using Machine Learning Techniques

Abstract

Faux news, one among the most important new-age problems has the potential to shape the opinions and influence decisions. The false information will spread more through social medias online. Fake news is often simply explained as a bit of article which is typically written for economic, personal or political gains. Recent political events have led to a rise within the popularity and spread of faux news. This proliferation of faux news on social media and internet is deceiving people to an extent which must be stopped. Faux news cannot be easily found by humans. They have to put lot of efforts in detecting faux news. The foremost popular of such attempts include “Blacklists” of sources and authors unreliable. While these tools are useful, so as to make a more complete end to finish solution, we would like to account for harder cases where reliable sources and authors release fake news. We used tongue processing techniques for Data pre-processing and have selection. The corpus labeled real and faux data is used for classification to yield good information for decision making. Five various models of classification are used and result analysis is carried out.