On September 2022, a massive online movement began on Iranian Twittersphere (Persian Twitter) in protest of the murder of a young girl, Mahsa Amini, by the Iran regime’s ‘morality police.’ It soon became the biggest protest against the regime’s brutality, mismanagement, and incapability, with more than 500 million tweets to date. This paper aims to investigate the political communication and networks on Persian Twitter during this happening. The empirical research data was collected from 15 September 2022 to 15 November 2022 from Twitter API (RIP :D). The data collection (with #MahsaAmini in Farsi) resulted in 232,500,839 tweets. I will preprocess the data by removing duplications, non-Persian, and spam tweets (i.e., tweets only use hashtags with no message.) Next, I will create the retweet (RT) network as the main network of information sharing on Twitter. Then, I will detect the main communities in the RT network using the Louvain algorithm.
In this step, I will identify the most influential users in each cluster using the PageRank centrality. A team of human coders will code them to identify their identity, type (journalist, politician, etc.), bot probability, and political orientation. I will also use automated text analysis (LDA2vec model) to investigate the most dominant topics in each community. Then, I will detect the most popular hashtags in each cluster. The human coders will examine these hashtags and topics further to investigate political communication across different communities on Persian Twitter.
The results will shed light on one of the biggest online protests in the history of Twitter. For instance, findings will indicate which clusters hosted more bots and to which aim they were being employed. In addition, the results will reveal how and by which mechanisms different types of users dominated topics and hashtags in various communities. The research will contribute to our understanding of Twitter activism in an understudies context where Twitter is blocked but still a significant channel for political communication: Iran.