Framing the #MahsaAmini movement on Persian Twitter: An analysis of networked framing in authoritarian regimes during anti-regime protests

This paper investigates actors and networked framing practices across different political communities on Persian Twitter during the recent #MahsaAmini movement. Drawing on the networked framing theory (Meraz & Papacharissi, 2013), this research aims to decentralize the existing literature in the field, which is mainly Western-centered. In addition, while most studies on Twitter activism in authoritarian regimes focus on non-democratic governments' roles, this study examines the behavior of different political groups, including pro- and anti-regime communities. Our empirical analyses focus on the Iranian Twittersphere during the #MahsaAmini movement. #MahsaAmini movement was a nationwide protest started in September 2022 in response to the murder of Mahsa Amini, a young Iranian woman. Many Iranians went on Twitter to show their rage against the regime’s brutality and policies. On the other hand, pro-regime users tried to defend the regime and support its ruling discourse. This contentious space provides a convenient context to study how different camps in authoritarian regimes try to dominate their frames and narratives. Thus, the following questions guide this research: RQ1: What were the main political groups active during #MahsaAmini movement on Persian Twitter? RQ2: Who were the most popular users, and what are their characteristics (e.g., career and identity) in each group? RQ3: What were the dominant frames in each group? RQ4: In which ways did the frames in each group conflict and compete with frames in other groups to dominate Persian Twitter? We collected all popular tweets using #MahsaAmini in Farsi form, i.e., tweets with more than 1k likes in a day (مهسا_امینی#) from September 15, 2022, to October 15, 2022. Our data collection resulted in a dataset of 58,088 Persian tweets sent by 10,308 unique users. We followed KhosraviNik’s (2017) approach to social media critical discourse analysis to analyze users and their tweets. Four human coders coded all users qualitatively and discursively in three consecutive rounds (Saldaña, 2015). Results show five main communities: pro-regime radical, pro-regime moderate, reformist, anti-regime radical, and anti-regime monarchists. We also coded each user's career (e.g., journalist), identity (e.g., anonymous), and bot probability, i.e., the extent to which a user is probably an automated account. Then, we extracted all tweets by users in each group. Following the same analytical approach, coders coded the tweet samples to investigate networked framing in each camp. We are in the middle of qualitative and discursive analysis now. Results will present the dominant frames in each community. In addition, we will map users to frames and compare such relations across all communities. Thus, the result will shed light on which type of users dominate what kind of frames in each camp. Finally, we will discursively analyze how dominant frames compete with each other in defining our understanding of a nationwide protest in an authoritarian regime. According to our plan, all analyses will be finalized by the end of February. Thus, the complete findings and conclusions will be ready before the conference. Results will enhance our understanding of networked framing in authoritarian regimes by focusing on an understudied context: Iran.