A Multi-Kernel Optimized Convolutional Neural Network With Urdu Word Embedding to Detect Fake News - Khurram Zaheer; Muhammad Ramzan Talib; Muhammad Kashif Hanif; Muhammad Umer Sarwar (IEEE Access, 2023)
One of the biggest threats to international trade, journalism, and democracy is fake news on social media, which also causes substantial collateral damage. The manipulation of digital news to spread misinformation has become a common practice for personal benefits or relief. Therefore, developing an automated system that can detect fake news before publication is crucial. This study proposes a three-level methodology with a new model called Multi-Kernel Optimized Convolutional Neural Network (MOCNN) to investigate its effectiveness for fake news detection. The parameters of the proposed model have been optimized using the grid search technique. We evaluated ten different deep learning models on two benchmark datasets of fake news articles and compared their performance with the proposed model. Finding the best model with good accuracy performance is the primary goal of this paper. F-measure and accuracy are used to evaluate and compare the classification performance of these deep learning models. Our proposed model achieves 85.8% and 68.2% accuracy and 85.8% and 67.7% F1-measure on UFN and BET, respectively. Experimental results confirm that the proposed model performs better than other models on UFN and BET datasets.