BNnet: A Deep Neural Network for the Identification of Satire and Fake Bangla News
Abdullah Al Imran, Zaman Wahid, Tanvir Ahmed
Abstract:
Misleading and fake news in rapidly increasing online news portals in Bangladesh has become a major concern to both the government and public lately, as a substantial amount of incidents have taken place in different cities due to unwarranted rumors over the last couple of years.
However, the overall progress of research and innovation in detecting fake and satire Bangla news is yet unsatisfactory considering the prospects it would bring to the decision-makers of Bangladesh. In this study, we have amalgamated both fake and real Bangla news from quite a pool of online news portals and applied a total of seven prominent machine learning algorithms to identify real and fake Bangla news, proposing a Deep Neural Network (DNN) architecture.
Using a total of five evaluation metrics: Accuracy, Precision, Recall, F1 score, and AUC, we have discovered that DNN model yields the best result with an accuracy and AUC score of 0.90 respectively while Decision Tree performs the worst.
Conference / Journal Link:
https://link.springer.com/chapter/10.1007/978-3-030-66046-8_38 DOI: 0.1007/978-3-030-66046-8_38
