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

Document Type : Research Paper

Authors
Hossein Kermani 1
Amirali Tafreshi 2
Amir Mohammad Ghodsi 3
Alireza Bayat Makou 4
Ali Atash Zar 5
1 Postdoctoral Researcher, Department of Communication, University of Vienna, Vienna, Austria. PhD in Social Communication Sciences, University of Tehran, Tehran, Iran.
2 PhD in Social Communication Sciences, University of Tehran, Tehran, Iran.
3 PhD student in Social Communication Sciences, Allameh Tabataba'i University, Tehran, Iran.
4 M.Sc. Student in Computer Science, Leibniz University Hannover, Hannover, Germany.
5 MA in Social Communication Sciences, Allameh Tabataba'i University, Tehran, Iran.
10.22054/nms.2022.57484.1103
Abstract
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 Results
The 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 Instagram during the initial days of the COVID-19 outbreak in Iran. Furthermore, the most prevalent micro-discourse formats, sporting the highest frequency counts, were selected for further analysis, resulting in five dominant constructs on Twitter and Instagram each. The investigation of the selected discourses provided insights into their facets and their connection with power dynamics in Iran. Three of the predominant discourse formats were found to be shared between both platforms, with each host possessing
Conclusions
This study endeavored to discover and examine the discourses manufactured by Iranian users during the COVID-19 outbreak. The psychological dynamics of these users in the early phases of the pandemic were analyzed in light of Network Framing Theory and Foucault's Discourse Theory. The outcome presents a comprehensive picture of the network agency of Persian users on Twitter and Instagram. Users on Twitter predominantly held a conviction to accuse parties deemed responsible for the outbreak, such as negligence, faith-based assumptions, concealment, and misrepresentation. Meanwhile, Instagram users were positioned as afflicted, either undertaking health preventive measures or suffering the fear and hardships of COVID-19 conditions. This study illustrates that although internet-based networks have encroached on the primacy of conventional media as a leading framing agency, their features and systems still exert a significant influence. Each network highlights distinct frames, with specific content production dynamics, thereby producing diverse discourse constructs. This case study assessed the responses of users during the initial period of the crisis (short-term), but additional research can examine its implications in the medium and long term.
Keywords
Coronavirus
Twitter
Instagram
Discourse Analysis
Foucault
Andersen, N. A. (2003). Discursive analytical strategies: understanding Foucault, Koselleck, Laclau, Luhmann. The Pol icy Press.
Baym, N. K., & Boyd, D. (2012). Socially Mediated Publicness: An Introduction. Journal of Broadcasting & Electronic Media, 56(3), 320–329. https://doi.org/10.1080/08838151.2012.705200
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008
Bruns, A., & Stieglitz, S. (2013). Towards more systematic Twitter analysis: metrics for tweeting activities. International Journal of Social Research Methodology, 16(2), 91–108. https://doi.org/10.1080/13645579.2012.756095
D’Antonio, E. (2020). audience. The Chicago School of Media Theory. https://lucian.uchicago.edu/blogs/mediatheory/keywords/audience/
Easley, D., & Kleinberg, J. (2010). Networks, Crowds, and Markets: A Book by David Easley and Jon Kleinberg. In Book. https://doi.org/10.1017/CBO9780511761942
Entman, R. M. (1993). Framing: Toward Clarification of a Fractured Paradigm. Journal of Communication, 43(4), 51–58. https://doi.org/10.1111/J.1460-2466.1993.TB01304.X
Fairclough, N. (1992). Discourse and social change. Blackwell Publishers.
Ferrara, E. (2020). What Types of Covid-19 Conspiracies Are Populated By Twitter Bots? ArXiv. https://doi.org/10.5210/fm.v25i6.10633
Foucault, M. (1972). Archaeology of Knowledge. In Archaeology of Knowledge. Routledge.
Galarza Molina, R. (2019). Networked Gatekeeping and Networked Framing on Twitter Protests in Mexico about the Ayotzinapa Case. International and Multidisciplinary Journal of Social Sciences, 8(3), 235. https://doi.org/10.17583/rimcis.2019.4637
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012–1014. https://doi.org/10.1038/nature07634
González-Bailón, S. (2015). Social Protest and New Media. In International Encyclopedia of the Social & Behavioral Sciences: Second Edition. https://doi.org/10.1016/B978-0-08-097086-8.95076-1
Hermida, A. (2010). TWITTERING THE NEWS: The emergence of ambient journalism. Journalism Practice, 4(3), 297–308. https://doi.org/10.1080/17512781003640703
Jackson, S.J., & Foucault Welles, B. (2016). #Ferguson is everywhere: initiators in emerging counterpublic networks. Information Communication and Society, 19(3), 397–418. https://doi.org/10.1080/1369118X.2015.1106571
Jackson, Sarah J., & Foucault Welles, B. (2015). Hijacking #MYNYPD: Social Media Dissent and Networked Counterpublics. Journal of Communication, 65(6), 932–952. https://doi.org/10.1111/JCOM.12185
Jackson, Sarah J., & Foucault Welles, B. (2016). #Ferguson is everywhere: initiators in emerging counterpublic networks. Information Communication and Society, 19(3), 397–418. https://doi.org/10.1080/1369118X.2015.1106571
Jensen, K. B. (2013). A Handbook of Media and Communication Research. In K. B. Jensen (Ed.), A Handbook of Media and Communication Research. Routledge. https://doi.org/10.4324/9780203465103
Jiang, M., Leeman, R. W., & Fu, K.-W. (2016). Networked Framing: Chinese Microbloggers’ Framing of the Political Discourse at the 2012 Democratic National Convention. Communication Reports, 29(2), 87–99. https://doi.org/10.1080/08934215.2015.1098715
Kermani, H., & Adham, M. (2021). Mapping Persian Twitter: Networks and mechanism of political communication in Iranian 2017 presidential election. Big Data and Society, 8(1). https://doi.org/10.1177/20539517211025568
Kermani, Hossein. (2020). Political narrating in non-political crises: narrativity practices on Persian Twitter during the 2017 Kermanshah earthquake. Asian Journal of Communication. https://doi.org/10.1080/01292986.2020.1817112
Khazraee, E. (2019). Mapping the political landscape of Persian Twitter: The case of 2013 presidential election. Big Data and Society, 6(1), 1–15. https://doi.org/10.1177/2053951719835232
Laclau, E., & Mouffe, C. (2001). Hegemony and Socialist Strategy: Towards a Radical Democratic Politics. Verso.
Lindgren, S. (2019). Movement Mobilization in the Age of Hashtag Activism: Examining the Challenge of Noise, Hate, and Disengagement in the #MeToo Campaign. Policy & Internet. https://doi.org/10.1002/poi3.212
Lombard, M., Snyder-Duch, J., & Bracken, C. C. (2002). Content Analysis in Mass Communication: Assessment and Reporting of Intercoder Reliability. Human Communication Research, 28(4), 587–604. https://doi.org/10.1111/J.1468-2958.2002.TB00826.X
Marchant, J., Ormson, T., Honari, A., & Sabeti, A. (2018). #iranvotes2017: Analysing the 2017 iranian presidential elections through Telegram, Twitter and Instagram.
Meraz, S., & Papacharissi, Z. (2013). Networked Gatekeeping and Networked Framing on #Egypt. International Journal of Press/Politics, 18(2), 138–166. https://doi.org/10.1177/1940161212474472
Mirbabaie, M., Bunker, D., Stieglitz, S., Marx, J., & Ehnis, C. (2020). Social media in times of crisis: Learning from Hurricane Harvey for the coronavirus disease 2019 pandemic response. Journal of Information Technology, 35(3), 195–213. https://doi.org/10.1177/0268396220929258
Molina, R. G. (2019). Networked gatekeeping and networked framing on twitter protests in Mexico about the ayotzinapa case | Networked gatekeeping y networked framing en las protestas en twitter sobre el caso ayotzinapa-méxico. International and Multidisciplinary Journal of Social Sciences, 8(3), 235–266. https://doi.org/10.17583/rimcis.2019.4637
Papacharissi, Z., & de Fatima Oliveira, M. (2012). Affective News and Networked Publics: The Rhythms of News Storytelling on #Egypt. Journal of Communication, 62(2), 266–282. https://doi.org/10.1111/J.1460-2466.2012.01630.X
Papacharissi, Z., & De Fatima Oliveira, M. (2012). Affective News and Networked Publics: The Rhythms of News Storytelling on #Egypt. Journal of Communication, 62(2), 266–282. https://doi.org/10.1111/j.1460-2466.2012.01630.x
Ross, K., & Nightingale, V. (2003). Media and audiences. https://books.google.com/books?hl=en&lr=&id=cyhEBgAAQBAJ&oi=fnd&pg=PP1&dq=Ross+%26+Nightingale,+2003&ots=inHjMhcXKP&sig=5gU0QVGTzoHIRboEOTg2ijaOW5Y
Rovetta, A., & Bhagavathula, A. S. (2020). Global Infodemiology of COVID-19: Focus on Google web searches and Instagram hashtags. https://doi.org/10.1101/2020.05.21.20108910
Sadler, N. (2018). Narrative and interpretation on Twitter: Reading tweets by telling stories. New Media & Society, 20(9), 3266–3282. https://doi.org/10.1177/1461444817745018
Starks, H., & Brown Trinidad, S. (2007). Choose Your Method: A Comparison of Phenomenology, Discourse Analysis, and Grounded Theory. Qualitative Health Research, 17(10), 1372–1380. https://doi.org/10.1177/1049732307307031
Taylor, D. B. (2020, August). A Timeline of the George Floyd Protests - The New York Times. NY Times. https://www.nytimes.com/article/george-floyd-protests-timeline.html
Weller, K., Bruns, A., Burgess, J., Mahrt, M., & Puschmann, C. (2014). Twitter and Society. In Twitter and Society. https://doi.org/10.1017/CBO9781107415324.004
Wonneberger, A., Hellsten, I. R., & Jacobs, S. H. J. (2020). Hashtag activism and the configuration of counterpublics: Dutch animal welfare debates on Twitter. Information, Communication & Society, 1–18. https://doi.org/10.1080/1369118X.2020.1720770
World Health Organization. (2020). Statement on the second meeting of the International Health Regulations. Emergency Committee Regarding the Outbreak of Novel Coronavirus (2019-NCoV). 2020.
 
References (In Persian)
Ejtehadi, M., & Keshafi Nia, V. (2019). Sociological Study of Celebrity Culture Audiences in Persian Instagram. 5(20), 133–148. https://doi.org/10.22054/NMS.2020.39301.673
ISPA. (2019). WhatsApp overtook Telegram/ The presence of Iranians in virtual social networks.
Tabrizi, Mansoureh. (2014). Qualitative content analysis from the perspective of comparative and inductive approaches. https://b2n.ir/x09599
Jafariyani, H. (2020). Typology of popular Instagram messages on the subject of Corona. Scientific Quarterly of Interdisciplinary Communications and Media Studies, 3(2), 113–148.
Khajeh Iyan, Datis; Salavatian, Siavash; Koli, Shaghayegh; Yazdani, Amirhossein. (2020). Behavioral model of Iranian digital natives on Instagram: Data mining in big social network data. Communication Research Quarterly, 27(101), 9–32. https://doi.org/10.22082/CR.2020.117695.1961 Soltani, S. A. (2012). Power, Discourse and Language (Mechanisms of Power Flow in the Islamic Republic of Iran). Ney Publishing.
Kermani, H. (2019). Social construction of reality in Persian Twitter; Network framing of political values by network publics in the 2017 presidential election. Political Science Research Journal, 14(4), 131–161.
Moradi, A., & Mohammadi Far, N. (2020). The role of social networks in shaping social fear and changing lifestyle due to Corona virus (Case study of Kermanshah city). Social Discipline Quarterly, 12(2), 123–148.
Ministry of Health, Treatment and Medical Education. (2020). Death of 415 COVID-19 patients in the country/ 6824 new cases infected. https://b2n.ir/533588
Jorgensen, M., & Phillips, L. J. (2010). Theory and Method in Discourse Analysis. Ney Publishing.