Optimizing LSTM Networks with Genetic and Q-Learning Algorithms for Persian COVID-19 Fake News Detection

—Currently, the development of the coronavirus as a pandemic and its global spread are a major concern for our society and the international community. In recent years, however, a growing number of people have transferred their primary source of news and information to social networks. So, the broad transmission of inaccurate and misleading information on social media is significant for the majority of politicians. Not only are we fighting against COVID-19, but also a "infodemic." To address this, on COVID-19, we have collected and released a labeled dataset of 7,000 Persian social media postings of true and fake news. Several languages, including Arabic, English, Chinese, and Hindi, have recognized Covid 19 fake news. This study utilizes a deep neural network approach to simplify feature extraction, develop a strong ability to learn, and automatically discover features compared to typical machine learning approaches, as well as a novel approach to improving outcome using a deep neural network. The genetic algorithm and reinforcement learning are provided for setting and optimizing the hyper-parameters of the deep learning algorithm, which has led to better outcomes than previous research and achieved an accuracy rate of 0.92 percent.