Published Paper Details:

: In our modern era where the internet is ubiquitous, everyone relies on online resources for news. Along with the increase in the use of social media platforms like Facebook, Twitter, etc. news spread rapidly among millions of users within a very short span of time. The spread of fake news has far-reaching consequences like the creation of biased opinions to swaying election outcomes for the benefit of certain candidates. Moreover, spammers use appealing news headlines to generate revenue using advertisements via click baits. It is harmful for the society to believe on the rumours and pretend to be a news. The need of an hour is to stop the rumours especially in the developing countries like India, and focus on the correct, authenticated news articles. Fake news detection is used to avoid rumours from spreading across various platforms, such as social media and messaging platforms. so, we aim to perform binary classification of various news articles available online with the help of concepts pertaining to Artificial Intelligence, Natural Language Processing and Machine Learning. We aim to provide the user with the ability to classify the news as fake or real and also check the authenticity of the website publishing the news. This model requires prerequisite data extracted from various news websites. Web scraping technique is used for data extraction which is further used to create datasets. The data is classified into two major categories which are true dataset and false dataset. Classifiers used for the classification of data is Logistic Regression. Based on the output received the data is classified either as true or false data. Based on that, the user can find out whether the given news is fake or not on the webserver.