Boosting Approach for Multiclass Fake News Detection

In the modern era of information, data integrity is of utmost priority. With the rapid development in the field of Artificial Intelligence, one who has credible data owns the key to build a reliable future. But with the breakneck development of communication over social media the reliability of data is no more guaranteed. “Fake News” is data that doesn’t have any real-world significance (or) a fact which has been modified by some middleman over the chain of communication. Spreading of such fake news affects humanity in various unacceptable perspectives. As a solution, in this paper, a machine learning approach is proposed to verify the trustworthiness of news. Instead of just classifying the data as true or fake, various degrees of truth and falsehood are also explored. The proposed methodology has been applied to “Liar, Liar Pants on Fire”, a benchmark data set for fake news detection. The proposed approach with 41.1% accuracy, outperforms the baseline approaches.