Abstract

AbstractThe dissemination of online misinformation is causing increasing concern around the world. Government officials and other responsible agencies are deeply worried about the situation and are working tirelessly to change it, but it seems that some yellow journalists are only interested in making money by selling news with clickbait headlines and fake facts inside the news. Some shady online news sources, both in English and Bengali, are actively working to spread false information in order to create a stir. While there are several systems that can detect fake news from English news corpus, we are unaware of any system that detects fake news from both Bengali and English languages at the same time. So, here we propose a bilingual fake news detection model in this paper that employs TF-IDF and N-Gram Analysis for feature extraction in order to detect fake news from a bilingual perspective. In addition, we compare the results of six separate machine learning algorithms for detecting false news. The model employs a supervised method of operation. Among these, we have acquired the highest performance with Linear Support Vector Classification (Linear SVC) algorithm where the accuracy is 93.29% and the F1 score is 0.93.KeywordsFake newsNLPBilingualText analysisTF-IDFText analysisN-gram modelsOnline news portals

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