COVID-19 on Iranian Twitter and Instagram: Discourse Analysis of Users’ Generated Content During the COVID-19 Pandemic

Introduction:The present study delves into the repercussions of the COVID-19 pandemic on human life and social interactions, with a particular focus on Iran. The pandemic has substantially impacted various facets of human life, resulting in diminishing physical presence in the public sphere to avoid getting infected with the virus, while increasing online interactions on social media platforms. The purposes of this research study include exploring the linguistic constructs developed on Twitter and Instagram in Farsi, during the initial stages of the COVID-19 outbreak in Iran. The analysis is aimed towards providing a comprehensive comprehension of the underlying meanings constructed and negotiated in the early days of Iran's experience with the COVID-19 crisis, particularly in relation to the presence of power dynamics and hegemonic discourses.Materials and Methods:The aforementioned study implements mixed methodologies, featuring a combination of computational and traditional qualitative approaches, namely SOCIAL network analysis and qualitative content analysis, to elevate the depth and validity of the analysis. Specifically, these methods are used to investigate the social networking components and discussive content present within social media. The data collected in this study entails more than 4 million tweets and Instagram submissions from January 21, 2020, to April 29, 2020. The focus of the Twitter data analysis centered on the retweet network, which acted as the information dispersion network. Following data refinement, the retweet network was extracted, comprising more than 2.5 million tweets. Using a modularity-based community detection algorithm, clusters within the retweet network were identified. Five significant clusters, boasting volumes in excess of 4% of the total network, were identified. Each cluster incorporated a selection of individuals identified as the most influential according to the Pagerank index, indicating the highest tweet circulation in the entire network. A sample of 5056 tweets representing the total tweet population (7658) was randomly drawn, following which they were qualitatively annotated via content analysis to identify the underlying discourses. The agreement coefficient, based on Krippendorff's Alpha, was calculated to be 83%.Discussion and ResultsThe findings of this research unveil a total of 71 micro-discourse constructs, clustered into 16 overarching macro-discourses, that were observed on both Twitter and Inst