Analyzing Deep Learning Optimizers for COVID-19 Fake News Detection Ayan Chakraborty and Anupam Biswas Abstract In this time of COVID-19 crisis, the threat posed by the propagation of misinformation leading to mistrust needs to be kept in check. Misinformation related to the vaccines, remedies, false symptoms, etc. are spiraling out of control. We might not be able to directly put a stop to the flow or spread of fake news to a large extent at the moment, but it may be able to identify it as such with the help of Natural Language Processing (NLP) tools and Deep Learning (DL) algorithms. Steps involved in achieving this goal can be narrowed down into collection and analysis of data from various sources, sorting out the articles as covid-relevant and categorizing them as real or fake using DL models. However, DL models use different optimizers in the learning process, which plays an important role in identifying the fake news. This chapter also compares the efficiency of different optimizers in the context of COVID-19 fake news detection using DL models. The newly developed Continuous Coin Betting (CoCoB) Optimizer for DL studied extensively for fake news detection and performed compared with four other widely used optimizers. The comparative analysis shows the CoCoB as well as popularly used Adam optimizers are quite effective in finding optimal classification results for detection of fake news related to COVID-19. Keywords Deep learning · Fake news · Misinformation · COVID-19 1 Introduction Over the past decade, social media has climbed up the ladder of connectivity and now carries the title of being one of the major sources of information that people come across on a daily basis. Yet, the news dissipated across social media falls on A. Chakraborty Department of Electronicsand Communications, Tezpur University, Tezpur, India A. Biswas (B ) Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, India e-mail: anupam@cse.nits.ac.in © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. Lahby et al. (eds.), Combating Fake News with Computational Intelligence Techniques, Studies in Computational Intelligence 1001, https://doi.org/10.1007/978-3-030-90087-8_20 401