Detection of Bangla Fake News using MNB and SVM Classifier

Abstract

Fake news has been coming into sight in significant numbers for numerous business and political reasons and has become frequent in the online world. People can get contaminated easily by these fake news for its fabricated words which have enormous effects on the offline community. Thus, interest in research in this area has risen. Significant research has been conducted on the detection of fake news from English texts and other languages but a few in Bangla Language. Our work reflects the experimental analysis on the detection of Bangla fake news from social media as this field still requires much focus. In this research work, we have used two supervised machine learning algorithms, Multinomial Naive Bayes (MNB) and Support Vector Machine (SVM) classifiers to detect Bangla fake news with CountVectorizer and Term Frequency -Inverse Document Frequency Vectorizer as feature extraction. Our proposed framework detects fake news depending on the polarity of the corresponding article. Finally, our analysis shows SVM with the linear kernel with an accuracy of 96.64% outperform MNB with an accuracy of 93.32%.

Hybrid Fake Information Containing Strategy Exploiting Multi-Dimensions Data in Online Community

“…Then, the experiment was finished using the Facebook news post dataset. In reference [25], the authors used the multinomial naive Bayes (MNB) and support vector machine (SVM) classifiers to detect Bangla fake news. From the experimental outcomes, it was proven that SVM exhibits a higher degree of accuracy than MNB methods.…”