clustering methods to analyze social media posts during coronavirus pandemic in iran

During the covid-19 crisis, we face a wide range of thoughts, feelings, and behaviors on social media that play a significant role in spreading information regarding covid-19. trustful information, together with during the covid-19 crisis, we face a wide range of thoughts, feelings, and behaviors on the social media that play a significant role in spreading information regarding covid-19. trustful information, together with hopeful messages, could be used to control the people's emotions and reactions during pandemics. this work examines the iranian society's resilience in the face of the corona crisis, and provides a strategy to promote resilience in similar situations. it investigates the posts and news related to the covid-19 pandemic in iran in order to determine which messages and references have caused concern in the community, and how they could be modified? and also which references are the most trusted publishers? the social network analysis methods such as clustering are used in order to analyze the data. in the present work, we apply a two-stage clustering method constructed on the self-organizing map and k-means. due to the importance of social trust in accepting messages, this work examines the public trust in social posts. the results obtained show that trust in the health-related posts is less than the social-related and cultural-related posts. the trusted posts are shared on instagram and the news sites. the health and cultural posts with negative polarity affect the people's trust, and lead to negative emotions such as fear, disgust, sadness, and anger. thus we suggest that non-political discourses are used in order to share the topics in the field of health.