Fake news detection with naive bayes classifier
Vishnuji Mishra, Manish verma, Arpit sharma
Naïve Bayes, Fake News, Machine Learning, Accuracy, Prediction.
Fake news have a huge impact on society. Fake news is being disseminated through an online-based lifestyle to reach an open audience. People use their web-based social networks to represent the only reason to spread fake news and spread the flames of falsehoods. Now people every day use social networks too much to update news. These networks aim to make social life better. Today, everyone knows and uses social media that contains an unconfirmed article, post, message and news. Today's false stories create a variety of issues from humorous stories to finding created stories and organizing government information in certain areas. Fake News and a lack of trust in the media exacerbate issues that have far-reaching implications for our society as a whole. It is necessary to consider how techniques in the field of computer science using computer-assisted reading techniques help us to find inaccurate information. Fake stories are now regarded as one of the greatest threats to freedom of speech, journalism and the country's democracy. In this study, a comprehensive method of detecting false information was introduced using a machine learning model trained by Fake News data is based on the latest Indian Fake news. This problem is resolved by the Machine Learning technique. In this research we are analysing the false stories(fake news) related problem that is divided into two part training and testing data set. After fitting Naïve Bayes Algorithm we have find the 98% accuracy.
Article Details
Unique Paper ID: 151486

Publication Volume & Issue: Volume 8, Issue 1

Page(s): 148 - 153
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