One of the new scientific ways of understanding discourse dynamics is analyzing the public data of social networks. This research's aim is Post-structuralist Discourse Analysis (PDA) of Covid-19 phenomenon (inspired by Laclau and Mouffe's Discourse Theory) by using Intelligent Data Mining for Persian Society. The examined big data is five million tweets from 160,000 users of the Persian Twitter network to compare two discourses. Besides analyzing the tweet texts individually, a social network graph database has been created based on retweets relationships. We use the VoteRank algorithm to introduce and rank people whose posts become word of mouth, provided that the total information spreading scope is maximized over the network. These users are also clustered according to their word usage pattern (the Gaussian Mixture Model is used). The constructed discourse of influential spreaders is compared to the most active users. This analysis is done based on Covid-related posts over eight episodes. Also, by relying on the statistical content analysis and polarity of tweet words, discourse analysis is done for the whole mentioned subpopulations, especially for the top individuals. The most important result of this research is that the Twitter subjects' discourse construction is government-based rather than community-based. The analyzed Iranian society does not consider itself responsible for the Covid-19 wicked problem, does not believe in participation, and expects the government to solve all problems. The most active and most influential users' similarity is that political, national, and critical discourse construction is the predominant one. In addition to the advantages of its research methodology, it is necessary to pay attention to the study's limitations. Suggestion for future encounters of Iranian society with similar crises is given.
翻译:最新的科学理解对话动态方法之一是分析社交网络的公共数据。 此项研究的目的是通过使用波斯社会智能数据挖掘( 由Laclau 和Mouffe的 Discion Theory ) 来分析Covid-19 现象( 由Laclau 和Mouffe的 Dision Theory ) 的结构化分析。 所审查的大数据是来自波斯推特网络的160,000名用户的500万次推特, 以比较两种讨论。 除了单独分析推文之外, 还根据retweets的关系, 建立了一个社交网络图形数据库。 我们使用VoteRank 算法来介绍和评级那些其职位成为口语的人, 只要网络上的信息传播范围最大化。 这些用户也根据他们的语言使用模式( 由Laclau 和Mouffeal Dism Dism ) 进行分组分析( ) ( ) ( ) ( ) ( ) ( ) (由最有影响力的传播者和最有影响力的用户组成者进行) (伊朗社会分析的结果。 ) ( ) ( ) (伊朗社会 ) 是, 最重要的研究的结果, 而不是伊朗社会是, 最有代表性的。