Fraudulent News Detection using Machine Learning Approaches
The rampant spread of fake news on social media has skyrocketed over the years. Fake news has become a notorious devil affecting the overall demographic of the nation. Its not only regular users who are worried but also the marketers who raised concerns about the impact of fake news on trade. Online sources for news consumption are a double edged sword. Fake news is increasingly becoming a menace to our society. It is typically generated for commercial interests to attract viewers and also to collect advertising revenue. However, media giants with potentially malicious agendas have been known to produce fake news in order to influence events and policies around the world. This paper addresses a classifier that can predict whether a piece of news is legit and not just a botched up fact. The proposed model train itself using data sets having headlines of news of multiple years to predict whether a news article is true to its word. The proposed work provides a convenient hassle free platform for everyone and aims to spread calm by decreasing rumors and misunderstandings in the society.
2019 Global Conference for Advancement in Technology (GCAT)