Bangle Fake News Detection Using Machine Learning Algorithms

Abstract

Advance modern technology and the growth of digitized content (using social media) manipulation users and turn to fake news, and its impact is deadly. Yellow news coverage is the cause of another big problem nowadays. Gain popularity and have profited through clickbait news publisher and social media-based news system circulated unauthorized news everywhere. The intention is to control religious, political, monetary, and other genuine things utilizing this simply get to strategy. The biggest concern is that it makes an annoyance and spreads savagery, even wage wars. Common people are not able to differentiate between fake and real news. The nature of fake news makes people suspects genuine news. Advance to use of NLP; it has ended up interesting to look for knowledge or designs within the era of fake news and thus find better prescient ways to discover fake news to categorize it from genuine news. In this paper, we propose an ML-based fake news detection strategy within the Bengali language. The proposed method uses a dataset on a LR, DT, RF, MNB, KNN, SVM algorithms. The calculation combination of TFIDF-based content features (Unigram, Bigram, Trigram) and vectorizing to include selection. The accuracy of our proposed model is 92.13 on the Multinomial Naive Bayes algorithm, which is the highest accuracy from other algorithms. In expansion to this, we have performed a comprehensive analysis of different machine learning algorithms. At the same time, we have completed a comprehensive study where we have conducted a literature review with some questions related to fake news, which has helped us acquire the knowledge required for this research.