Telegram Discourse on The 2017 Iran’s Presidential Election

Asian Journal of Media and Communication E-ISSN: 2579-6119, P-ISSN: 2579-6100 Volume 4, Number 1, April 2020 Telegram Discourse on the 2017 Iran’s Presidential Election Hossein Kermani University of Tehran Abstract This paper explores the discourse in Telegram during the Iran’s 2017 election period. Recently, Telegram has become the most popular social media in Iran, playing a significant role in recent social and political events. This paper examines the highest-ranking posts to provide a better understanding of Telegram posts’ characteristics, dynamics, and potentials for producing the new discourses or reinforcing the existing ones. The materials of this study were gathered from the 620 most-viewed posts during the election period. The data were analyzed mainly with quantitative content analysis and completed with ethnographic content analysis. This study finds that counter discourses are not shaped in Telegram and the most viewed posts mainly reproduce and reinforce the dominant discourses. This finding also clarifies how the content circulation is affected by the national political events, such as the election. Keywords: Telegram, Iran, presidential election, content analysis, discourse analysis. Introduction This paper tries to analyze the most viewed posts in a favorite instant messaging application (IMA) in Iran called Telegram. Although recently scholars have paid more attention to social media in Iran (Alavi, 2005; Faris & Rahimi, 2016; Iran Media Program, 2014; Mottahedeh, 2015), there have not been many studies concerning Telegram as a research field. Considering its popularity in Iran, doing such studies is necessary. Therefore, we focus on this application, especially its channels, to understand what types of posts and content are more popular and how they represent Iranian attitudes in social media. Analyzing the mostviewed posts can tell us much about users’ preferences, tastes, motivations and reading habits. Moreover, we can identify channels which have the main role in content circulation and analyze the affordances and potentials that give them such position. We carried out this study in the 2017 presidential election to identify how such important social and political events affect Telegram. Finally, we will be able to scrutinize the links between these posts and the dominant discourses in Iran to find out if there are any determinative (even productive) impacts. However, Telegram has two domains to host its traffic. Telegram.org is allocated to personal chats, and Telegram.me hosts its channels and groups, both are popular in Iran. In fact, in June 2017, 69.9 percent of Telegram.me’s visitors were Iranians. Furthermore, there are more than 380 thousand Persian channels in Telegram and more than 2 million posts are published in them daily. This is 15 Volume 4, Number 1, April 2020, 15-29 an unprecedented situation in the social media history in Iran and is the outcome of Telegram being ubiquitous. Moreover, some of these unofficial channels have many members. For instance, @akhbarefori (InstantNews) had over 1.7 million users in June 2017 or @GizmizTel had more than 1.5 million members at the same time. Additionally, analyzing political discussions in various platforms, especially during elections, has been an interesting subject for most scholars. At first, the researchers focused on internet as a general platform (Papacharissi, 2004; Schneider, 1996), but as social media became more diverse, political debates in different networks such as Facebook, Twitter, etc. were examined (Hopke & Simis, 2017; Ji, 2016; Larsson, 2017; Miller, Bobkowski, Maliniak, & Rapoport, 2015; Saebo, Rose, & Molka-Danielsen, 2010). Therefore, we decided to conduct this study during the 2017 presidential election. Thus, we will able to show if social or political events affect the pattern of content distribution in Telegram or the characteristics of posts. Moreover, we can examine the relationship between the most viewed posts, and social and political discourses to see if they reconstruct or reshape the discourses. Method This study has applied content analysis, both quantitative and qualitative, to extract the manifest and latent meaning of posts, in addition to describing their formal attributes. Then, we blended these with ethnographic content analysis to validate, expand and verify interpretations. Using this method enabled us to go beyond statistical tables, which is common in quantitative methods, and discover narratives rather than numbers. In order to identify how social reality is constructed and represented in Telegram’s channels, we added another level to 16 our examinations. In fact, quantitative content analysis only provided us with some simple numerical data. However, by applying qualitative and ethnographic methods we made more textual interpretations rather than just statistical ones. Content analysis is a popular method in social science and communication fields (Krippendorff, 2004; Prasad, 2008; Riffe, Lacy, & Fico, 2014). In fact, there are many definitions of content analysis dealing with different aspects of this method. However, we based this research on Riffe et al. definition (2014) which sees content analysis as the systematic assignment of communication content to categories according to rules, and the analysis of relationships involving those categories using statistical methods. Moreover, based on Schreier's (2014) suggested plan, we began the content analyses by creating the research questions (which are articulated in the previous section) and selecting the sample. The research sample consisted of the most viewed posts in Telegram. In fact, Telegram puts a sign under each post (an eye icon) to show the number of views and we used it as a benchmark for measuring a post’s popularity. Of course it is a quantitative measure and we cannot conclude that a post with higher views is necessarily more popular. However, it is the only standard that we have for measuring the posts’ popularity on a big scale. Therefore, we used it with caution, by acknowledging its limitations. In the next step, we collected the 20 most viewed posts, daily from May 3 to June 2, 2017. In fact, we began the process 16 days before the Election Day (May 19, 2017) and continued it to 14 days later. As a result, we gathered 620 posts with the highest views in each day in the election period. The bot designed by Social Network Lab of Tehran University (@tlgrphy) was utilized to identify the most viewed posts. Preparing the sample, we built the primary code schema (based on our experiences and anticipations as Hossein Kermani, Telegram Discourse on the 2017 Iran’s Presidential Election Telegram users) and four coders were employed to code the posts. Meanwhile, the coding frame was modified and edited by coders to reach a final sheet. Then, they coded the posts again based on the new frame. Quantitative and qualitative analyses methods were blended in taking these s (...truncated)