This study employs advanced deep learning models, including convolutional neural networks (CNN), recurrent neural networks (RNN), hybrid architectures, bidirectional long short-term memory (BiLSTM) networks, and transformers, to analyze sentiment in COVID-19 and Omicron-related tweets. The goal is to explore the relationship between social media popularity and classification accuracy while addressing challenges associated with false information during the pandemic. The research aims to enhance accuracy in identifying misinformation, offering insights for public health, digital literacy, and crisis management. Comparative analysis of the models reveals their strengths and weaknesses, establishing a benchmark for future misinformation detection studies. While emphasizing the importance of accurate information during crises, the study acknowledges limitations such as a lack of multilingual analysis, Twitter-centric focus, and potential bias in sentiment analysis datasets. The difficulties in interpreting massive neural networks and the transformative impact of social media on information dissemination are also recognized. Results showcase accuracy metrics for different classifiers, highlighting variations in sentiment analysis performance across datasets. In conclusion, the study contributes to understanding misinformation complexities during the pandemic, providing a nuanced analysis of sentiment in social media. It establishes a foundation for future studies on misinformation detection, emphasizing the crucial role of accurate information in navigating global challenges. However, it falls short in detailing potential social and regulatory repercussions from social media restrictions.