Fake News Detection Using Optimized CNN and LSTM Techniques - Emmy Danny Ajik; Georgina N Obunadike; Faith O Echobu (Universitas Bina Darma, 2023)
Concerns have been raised about the social consequences of fake news as it has spread rapidly on online platforms. It is critical to detect and mitigate the spread of fake news in order to maintain a healthy community conversation. There is a need to put more effort into the identification of fake news as more people use the internet, especially as more internet-enabled gadgets become more widely available and inexpensive. With the help of two Neural Network techniques: long-short-term memory (LSTM) and Convolutional Neural Network (CNN). This research proposes novel deep-learning methods for identifying fake news using two datasets. These methods were considered for this research because they had proven to be successful in earlier studies that had been looked at. Finding the best-performing optimal models is the goal of this study. HyperOpt Technique was used for Neural Network model. The performance of the optimized models was compared with the performance of the models without optimization. The results obtained showed that for both datasets, CNN and LSTM performed better when training the models with the optimal values with an average difference of 12.7% for Accuracy, 11.9% for Precision, 12.3% for Recall and 15.4% for F1-Score.