The coronavirus disease (COVID-19) outbreak was declared a pandemic in March 2020 and since then it has had a significant effect on all aspects of life. Although we live in an information era, we do not have accurate information about this disease. Online social networks (OSNs) play a vital role in society, especially people who do not have trust in the government would tend to have more confidence in the evidence that is formed by social networks. The advantages of OSNs in the COVID-19 era are clear. For instance, social media enables people to connect with each other without the need for real-world face-to-face social interaction. Social media networks also act as a collective intelligence in the absence of world leadership. Therefore, in this study, considering the phenomenon of information diffusion in OSNs, we focus on the effects of COVID-19 on user sentiment and show the user behavior trend during the early months of the pandemic through mining and analyzing OSN data. Moreover, we propose a data-driven model to demonstrate how user sentiment changes over a period of time and how OSNs help us to obtain information on user behavior that is very important for the accurate prediction of future behavior. For this purpose, this study uses tweet texts about COVID-19 and the related network structure to extract significant features, and then presents a model attempting to provide a more comprehensive real picture of current and future conditions.