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@article{151398,
        author = {Dr. Rachna Somkunwar and Anil Kumar Gupta and Faizan Shikalgar and Shubham Maral and Arpit Paithankar and Rohan Kapdi},
        title = {Fake News Detection : A Survey},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {12},
        pages = {664-669},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=151398},
        abstract = {To address the modern society’s need for reliable News from media has been a challenge since the growth of recent technologies and advancements made in it. With the technological advancements, online news is more exposed to users all around the world and facilitates to increase in spreading disinformation online. Various artificial intelligence tools have been used to solve this problem of fake news identification. Fake news leads to convince the reader to believe fake information which proves these articles difficult to read.Fake news detection problem can be solved by the help of Artificial Intelligence algorithms which includes machine learning Machine Learning algorithms. Our algorithm for detecting wrong information is based on the stance detection. The best way to handle this situation is, not by examining the facts, but by comparing how well-respected sources feel about such claims. We feel that our system based on the stance detection provides greater reliability. We aim for a  way to distinguish articles from unknown sources such as agreeing or disagreeing with known sources. We were able to achieve an accuracy of 82% by using stance detection model.},
        keywords = {Fake News, Artificial Intelligence, Machine Learning, Stance detection.},
        month = {},
        }