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One of the new scientific ways of understanding discourse dynamics isanalyzing the public data of social networks. This research's aim isPost-structuralist Discourse Analysis (PDA) of Covid-19 phenomenon (inspired byLaclau and Mouffe's Discourse Theory) by using Intelligent Data Mining forPersian Society. The examined big data is five million tweets from 160,000users of the Persian Twitter network to compare two discourses. Besidesanalyzing the tweet texts individually, a social network graph database hasbeen created based on retweets relationships. We use the VoteRank algorithm tointroduce and rank people whose posts become word of mouth, provided that thetotal information spreading scope is maximized over the network. These usersare also clustered according to their word usage pattern (the Gaussian MixtureModel is used). The constructed discourse of influential spreaders is comparedto the most active users. This analysis is done based on Covid-related postsover eight episodes. Also, by relying on the statistical content analysis andpolarity of tweet words, discourse analysis is done for the whole mentionedsubpopulations, especially for the top individuals. The most important resultof this research is that the Twitter subjects' discourse construction isgovernment-based rather than community-based. The analyzed Iranian society doesnot consider itself responsible for the Covid-19 wicked problem, does notbelieve in participation, and expects the government to solve all problems. Themost active and most influential users' similarity is that political, national,and critical discourse construction is the predominant one. In addition to theadvantages of its research methodology, it is necessary to pay attention to thestudy's limitations. Suggestion for future encounters of Iranian society withsimilar crises is given.

Discourse Analysis of Covid-19 in Persian Twitter Social Networks Using Graph Mining and Natural Language Processing