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The coronavirus disease (COVID-19) outbreak was declared a pandemic in March2020 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 informationabout 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 havemore confidence in the evidence that is formed by social networks. Theadvantages of OSNs in the COVID-19 era are clear. For instance, social mediaenables people to connect with each other without the need for real-worldface-to-face social interaction. Social media networks also act as a collectiveintelligence in the absence of world leadership. Therefore, in this study,considering the phenomenon of information diffusion in OSNs, we focus on theeffects of COVID-19 on user sentiment and show the user behavior trend duringthe early months of the pandemic through mining and analyzing OSN data.Moreover, we propose a data-driven model to demonstrate how user sentimentchanges over a period of time and how OSNs help us to obtain information onuser behavior that is very important for the accurate prediction of futurebehavior. For this purpose, this study uses tweet texts about COVID-19 and therelated network structure to extract significant features, and then presents amodel attempting to provide a more comprehensive real picture of current andfuture conditions.

User Behavior Discovery in the COVID-19 Era through the Sentiment Analysis of User Tweet Texts