OBJECTIVE The main objective is to detect the fake news, which is a classic text classification problem with a straight forward proposition. It is needed to build a model that can differentiate between “Real” news and “Fake” news
INTRODUCTION These days’ fake news is creating different issues from sarcastic articles to a fabricated news and plan government propaganda in some outlets. Fake news and lack of trust in the media are growing problems with huge ramifications in our society. Obviously, a purposely misleading story is “fake news “ but lately blathering social media’s discourse is changing its definition. Some of them now use the term to dismiss the facts counter to their preferred viewpoints.
The importance of disinformation within American political discourse was the subject of weighty attention , particularly following the American president election . The term ‘fake news’ became common parlance for the issue, particularly to describe factually incorrect and misleading articles published mostly for the purpose of making money through page views. In this paper,it is seeked to produce a model that can accurately predict the likelihood that a given article is fake news.
Facebook has been at the epicenter of much critique following media attention. They have already implemented a feature to flag fake news on the site when a user sees’s it ; they have also said publicly they are working on to to distinguish these articles in an automated way. Certainly, it is not an easy task. A given algorithm must be politically unbiased – since fake news exists on both ends of the spectrum – and also give equal balance to legitimate news sources on either end of the spectrum. In addition, the question of legitimacy is a difficult one.However, in order to solve this problem, it is necessary to have an understanding on what Fake News is. Later, it is needed to look into how the techniques in the fields of machine learning, natural language processing help us to detect fake news.
REQUIREMENTS 1.Python 2.numpy 3.pandas 4.matplotlib 5.sklearn
Results
Using a dataset, implemented a TfidfVectorizer, initialized a PassiveAggressiveClassifier, and fit our model. We ended up obtaining an accuracy of 88.95% in magnitude.