Malayalam Fake News Detection Using Optimized Convolutional Neural Network (OPCNN)

These days, a range of issues are being caused by fake news, including satirical pieces, fake news, and intentional government propaganda. Because of the blathering social media discourse, the idea of "fake news" has recently evolved. Wide-ranging consequences of fake news dissemination include the development of distorted opinions and the tampering of election outcomes to benefit specific politicians. Spammers also use captivating news headlines to entice readers to click on their advertisements. These days, it can be quite difficult to tell if the news we hear is accurate. There aren’t many methods for assessing authenticity, and the ones that are accessible are all difficult and beyond the capabilities of the average person. A fact-checking platform that gives us that chance is desperately needed. Deep Learning (DL) approaches are suited for more accurately and efficiently detecting false news because of their recent accomplishments in difficult natural language processing tasks. The suggested approach is a multi-layered, optimal CNN model for detecting fake news. The OPCNN and Word embedding are optimized for feature extraction using the hyperopt optimization method in order to produce highly accurate results. In this study, we generated word embeddings using the FastText technique. The purpose of the model is to identify news stories published in Malayalam that are either false or misleading. The model is trained on 80% of a newly compiled Malayalam dataset consisting of news articles from various online sources having 11,686 news items with 5843 false and real news entries apiece and then evaluated on the remaining 20%. The evaluation’s findings show that the suggested model works better than current methods and has a high degree of accuracy when it comes to identifying false information in Malayalam.