A Novel Score-Based Multi-Source Fake News Detection using Gradient Boosting Algorithm

The dissemination of fake news in an online social network platform has been a real concern nowadays. Through social media, news articles are posted by many sources like news channels, websites, or even newspaper websites. There is a need to be sure that the information posted is only from credible sources and these posts have to authenticate. The intensity of genuineness of the news posted online cannot be measured absolutely and is still challenging. A novel Score -based Multi-Source Fake News Detection framework is proposed in this work to automate the detection of fake news from multiple news sources. This framework extracts the text-based features from genuine and fake news articles using Term Frequency - Inverted Document Frequency. Then the credibility score of the source s is calculated based on the site_url features and Top Level Domain. By assimilating the text-based features with the credibility score of multi-source, the credibility of the news is estimated. The proposed framework is applied to the Machine Learning (ML) classifiers to examine their performance in the detection of fake news. The experimental results determine the efficacy of the proposed framework with the Gradient Boosting algorithm of about 99.5% to the utmost level.