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 hopeful messages, could be used to control people's emotions and reactions during pandemics. This study examines Iranian society's resilience in the face of the Corona crisis and provides a strategy to promote resilience in similar situations. It investigates posts and news related to the COVID-19 pandemic in Iran, to determine which messages and references have caused concern in the community, and how they could be modified? and also which references were the most trusted publishers? Social network analysis methods such as clustering have been used to analyze data. In the present work, we applied a two-stage clustering method constructed on the self-organizing map and K-means. Because of the importance of social trust in accepting messages, This work examines public trust in social posts. The results showed trust in the health-related posts was less than social-related and cultural-related posts. The trusted posts were shared on Instagram and news sites. Health and cultural posts with negative polarity affected people's trust and led to negative emotions such as fear, disgust, sadness, and anger. So, we suggest that non-political discourses be used to share topics in the field of health.