Detection of Bangla Fake News using MNB and SVM Classifier

TL;DR

This paper presents an experimental analysis for detecting Bangla fake news using machine learning classifiers, achieving high accuracy with SVM and MNB on social media data.

Contribution

It introduces a novel application of SVM and MNB classifiers for Bangla fake news detection, a language with limited prior research in this area.

Findings

SVM with linear kernel achieves 96.64% accuracy.

MNB classifier achieves 93.32% accuracy.

Feature extraction using CountVectorizer and TF-IDF enhances detection performance.

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%.