A conventional & deep learning strategy for analyzing & detecting Bengali fake news in online medium

Abstract

Nowadays, social networking sites like Facebook and Twitter have become an sig- nificant impact on our lives . We use such sites to remain in touch with one another and as a source of news to stay informed about current events. As a result, we frequently see news articles with click-bait headlines from various web portals that lack authenticity. The majority of these sites that share these sorts of links are used to manipulate people and spread false propaganda. We intended to utilize both traditional machine learning algorithms and deep learning algorithms on manually annotated data-sets to create effective approaches for spotting Bangla fake news on online media that included about 8,500 pieces of news data. In particular, in this project, we used classic machine learning algorithms for text classification such as “Naive Bayes Classifier”, “Support Vector Machines (SVM)”, “K-Nearest Neigh- bor (KNN)” as well as other classification-based algorithms such as “Decision Tree (DT)”, “Logistic Regression (LR)”, “Random Forest” and “AdaBoost”. We have also used deep learning models based on Feed-Forward Neural Networks such as “Convolutional Neural Network (CNN)” as well as a variety of Recurrent Neural Networks (RNN) such as “Long-Short Term Memory (LSTM)”, “Gated Recurrent Unit (GRU)” to detect fake news on online media. To conclude, our research focused on developing precise strategies for spotting fake news on social media sites.