The COVID-19 Infodemic: Misinformation About Health on Social Media in Istanbul

Yıl 2022, Sayı: 40, 340 - 358, 30.06.2022

Serdar Tunçer , Mehmet Sinan Tam

https://doi.org/10.17829/turcom.1050696

Cited By: 4

Öz

Misinformation and conspiracy theories can spread as quickly as the COVID-19 pathogen itself. The infodemic, which describes false or misleading information about this recent epidemic on the internet, has become a serious problem all over the world, and has been declared as an “enemy” by the World Health Organization. In this sense, in order to combat the epidemic, it becomes important to reveal the nuances of COVID-19 related infodemic available on the internet. Particularly, internet users in Turkey are increasingly utilizing social media –a platform synonymous with misinformation– to access news coverage regarding the pandemic (World Health Organization, 2020). In this quantitative study focusing on the city of Istanbul (n=399), which is at the epicenter of the outbreak in Turkey, the social media usage of individuals, their trust in these platforms, exposure to misinformation and conspiracy theories, and fact-checking behaviors were examined. Our results indicate that participants tended to believe in misinformation and conspiracy theories rather than confirming information through fact-checking platforms. Nearly half of all participants believed at least one of four widespread conspiracy theories about the virus. Moreover, when fact-checking did identify misinformation, the participants’ trust in social media showed a slight decrease. Based on these findings, our study proposes a comprehensive model for pandemic-related trust, misinformation, conspiracy theories, and fact-checking factors on digital platforms.

Anahtar Kelimeler

Covid-19 Infodemic, Misinformation, Conspiracy Theories, Fact-Checking, Social Media

COVID-19 İnfodemi: İstanbul Örneklemi Kapsamında Nicel Bir Araştırma

Öz

Yaşanan COVID-19 salgını, yanlış-yalan haber ve komplo teorilerinin, salgının kendisi kadar hızlı yayılabildiğini ortaya koymuştur. İnternetteki salgın ile ilgili yalan-yanlış bilgileri tarif eden infodemi, tüm dünyada ciddi bir sorun haline gelmiş ve Dünya Sağlık Örgütü (2020) tarafından “düşman” ilan edilmiştir. Bu anlamıyla, salgın ile mücadele etmek için, internette bulunan COVID-19 ile ilgili infodeminin nüanslarını ortaya koymak önem kazanmaktadır. Türkiye’deki internet kullanıcıları, salgın ile ilgili haberlere erişmek için, yanlış bilgilerin de çokça paylaşıldığı sosyal medya platformlarını kullanmaktadır. Salgının merkez üslerinin başında gelen İstanbul (n = 399) kentine odaklanan bu nicel çalışmada, bireylerin sosyal medya kullanımları, bu platformlara olan güvenleri, yanlış bilgi ve komplo teorilerine maruz kalışları ve son olarak bilgileri teyit etme alışkanlıkları incelenmiştir. Çalışmamız, katılımcıların gerçek bilgiyi teyit etmeden, yanlış bilgilere ve komplo teorilerine inanma eğiliminde olduklarını göstermektedir. Katılımcıların yaklaşık yarısı, virüsle ilgili tespit edilen dört yaygın komplo teorisinden en az birine inandığını belirtmektedir. Bununla birlikte, katılımcıların inanma eğiliminde oldukları haberin/bilginin yanlış çıkması halinde, sosyal medyaya olan güvenlerinde nispi bir azalma olduğu anlaşılmaktadır. Çalışmamız bu bulgular ışığında, dijital platformlar ve pandemi bağlamında güven, yanlış bilgi, komplo teorileri ve teyit faktörlerini kapsayan bir model önermektedir.

Anahtar Kelimeler

Kovid-19 Infodemi, Yanliş Bilgi, Komplo Teorileri, Teyit, Sosyal Medya

Kaynakça

• Akyüz, S. S. (2021). Aşı karşıtlığı ve şeffaflık algısında iletişim pratikleri ve siyasal aidiyetlerin rolü. Yeni Medya Elektronik Dergisi, 5(2), 172–185.
• Alper, S., Bayrak, F., & Yilmaz, O. (2021). Psychological correlates of COVID-19 conspiracy beliefs and preventive measures: Evidence from Turkey. Current Psychology, 40(11), 5708–5717.
• Andı, S., Aytaç, S. E., & Çarkoğlu, A. (2020). Internet and social media use and political knowledge: Evidence from Turkey. Mediterranean Politics, 25(5), 579–599.
• Ashrafi-rizi, H. & Kazempour, Z. (2020). Information typology in coronavirus (COVID-19) crisis; a commentary. Archives of Academic Emergency Medicine, 8(1), e19.
• Binark, M., Arun, Ö., Özsoy, D., Kandemir, B., & Şahinkaya, G. (2020). Covid-19 sürecinde yaşlıların enformasyon arayışı ve enformasyon değerlendirmesi. TRDizin.gov.tr. Retrieved July 31, 2021 from https://app.trdizin.gov.tr/proje/TWpFME9EYzM/covid-19-surecinde-yaslilarin-enformasyon-arayisi- ve-enformasyon-degerlendirmesi.
• Bollen, K. A. (1989). A new incremental fit index for general structural equation models. Sociological Methods & Research, 17(3), 303–316. Brotherton, R., French, C., & Pickering, A. (2013). Measuring belief in conspiracy theories: The generic conspiracist beliefs scale. Frontiers in Psychology, 4(279), 1-15.
• Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd edition). New York: The Guilford Press.
• Chayinska, M., Uluğ, Ö. M., Ayanian, A. H., Gratzel, J. C., Brik, T., Kende, A., & McGarty, C. (2021). Coronavirus conspiracy beliefs and distrust of science predict risky public health behaviours through optimistically biased risk perceptions in Ukraine, Turkey, and Germany. Group Processes & Intergroup Relations, 1-19, https://doi.org/10.1177/136.843.0220978278.
• Chou, W.-Y. S., Oh, A., & Klein, W. M. P. (2018). Addressing health-related misinformation on social media. JAMA, 320(23), 2417–2418.
• Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., Zola, P., Zollo, F., & Scala, A. (2020). The COVID-19 social media infodemic. Scientific Reports, 10(1), 1-10.
• Çömlekçi, M. F. & Başol, O. (2019). Sosyal medya haberlerine güven ve kullanıcı teyit alışkanlıkları üzerine bir inceleme. Galatasaray Üniversitesi İletişim Dergisi, 30, 55–77.
• Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, phone, mail, and mixed-mode surveys: The tailored design method (4th edition). New Jersey: Wiley Publishing.
• Flynn, B. B., Sakakibara, S., Schroeder, R. G., Bates, K. A., & Flynn, E. J. (1990). Empirical research methods in operations management. Journal of Operations Management, 9(2), 250–284.
• Gelfert, A. (2018). Fake news: A definition. Informal Logic, 38(1), 84–117.
• Golman, R., Hagmann, D., & Loewenstein, G. (2017). Information avoidance. Journal of Economic Literature, 55(1), 96–135.
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th edition). New Jersey: Pearson.
• Hu, L. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.
• Islam, M. S., Sarkar, T., Khan, S. H., Mostofa Kamal, A.-H., Hasan, S. M. M., Kabir, A., Yeasmin, D., Islam, M. A., Amin Chowdhury, K. I., Anwar, K. S., Chughtai, A. A., & Seale, H. (2020). COVID-19–related infodemic and its impact on public health: A global social media analysis. The American Journal of Tropical Medicine and Hygiene, 103(4), 1621–1629.
• Kemp, S. (2020). Digital 2020: Turkey. Global digital insights. Datareportal. Retrieved May 12, 2021 from https:// datareportal.com/reports/digital-2020-turkey.
• Kılıç, D. & İspir N.B. (2017). Sosyal medyada kullanıcının haber güvenilirliği algısı. Paper presented at 1. Uluslararası İletişimde Yeni Yönelimler Konferansı, İstanbul: İstanbul Ticaret Üniversitesi, pp. 402-409. 4-5 May, 2017. Retrieved April 2, 2021 from http://acikerisim.ticaret.edu.tr/xmlui/handle/11467/1634. Larson, H. J. (2018). The biggest pandemic risk? Viral misinformation. Nature, 562(7726), 309–310.
• Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., Schudson, M., Sloman, S. A., Sunstein, C. R., Thorson, E. A., Watts, D. J., & Zittrain, J. L. (2018). The science of fake news. Science, 59(6380), 1094-1096.
• Li, Y.-J., Cheung, C. M. K., Shen, X.-L., & Lee, M. K. O. (2019). Health misinformation on social media: A literature review. PACIS 2019 Proceedings. Retrieved July 10, 2021 from https://aisel.aisnet.org/ pacis2019/194.McIntyre, L. (2018). Post-truth (Second edition). London: MIT Press. Ministry of Health. (2021). [Government]. COVID-19 bilgilendirme platformu. Retrieved June 21, 2021 from https://covid19.saglik.gov.tr/.
• Nielsen, K., Fletcher, R., Newman, N., & Brennen, S. (2020). Navigating the ‘infodemic’: How people in six countries access and rate news and information about coronavirus. Reuters Institute for the Study of Journalism. Retrieved February 21, 2021 from https://reutersinstitute.politics.ox.ac.uk/infodemic-how- people-six-countries-access-and-rate-news-and-information-about-coronavirus. Nyhan, B. & Reifler, J. (2010). When corrections fail: The persistence of political misperceptions. Political Behavior, 32(2), 303–330.
• Oh, O., Agrawal, M., & Rao, H. R. (2013). Community intelligence and social media services: A rumor theoretic analysis of tweets during social crises. MIS Quarterly, 37(2), 407–426.
• Pariser, E. (2012). The filter bubble: How the new personalized web is changing what we read and how we think. New York: Penguin.
• Pentina, I. & Tarafdar, M. (2014). From “information” to “knowing”: Exploring the role of social media in contemporary news consumption. Computers in Human Behavior, 35, 211–223.
• Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2014). Technology acceptance model (TAM) and social media usage: An empirical study on Facebook. Journal of Enterprise Information Management, 27(1), 6–30.
• Rini, R. (2017). Fake news and partisan epistemology. Kennedy Institute of Ethics Journal, 27(S2), 43–64.
• Suler, J. (2004). The online disinhibition effect. CyberPsychology & Behavior, 7(3), 321–326.
• Tangcharoensathien, V., Calleja, N., Nguyen, T., Purnat, T., D’Agostino, M., Garcia-Saiso, S., Landry, M., Rashidian, A., Hamilton, C., AbdAllah, A., Ghiga, I., Hill, A., Hougendobler, D., Andel, J. van, Nunn, M., Brooks, I., Sacco, P. L., Domenico, M. D., Mai, P., ... Briand, S. (2020). Framework for managing the COVID-19 infodemic: Methods and results of an online, crowdsourced WHO technical consultation. Journal of Medical Internet Research, 22(6), 5-11.
• Tunçer, S. (2018). Reinterpreting crisis communications in the post-truth era. Moment Dergi, 5(2), 368–382. Tuncer, S. & De B’béri, B.E. (2021). Social media and the changing discourse of immigration. In U. Bakan & M. L. Lengel (Eds.) Social media archaeology from theory to practice (pp. 215-229). London: MacroWorld Pub.
• Ünver, A. (2020). Fact-checkers and fact-checking in Turkey. EDAM Research Reports. Retrieved May 12, 2021 from https://ssrn.com/abstract=3644285.
• van der Meer, T. G. L. A. & Jin, Y. (2020). Seeking formula for misinformation treatment in public health crises: The Effects of corrective information type and source. Health Communication, 35(5), 560–575. Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151.Retrieved June 12, 2021 from https://www.science.org/doi/10.1126/science.aap9559.
• Wang, Y., McKee, M., Torbica, A., & Stuckler, D. (2019). Systematic literature review on the spread of health- related misinformation on social media. Social Science & Medicine, 240(112552), 1–12.
• Waszak, P. M., Kasprzycka-Waszak, W., & Kubanek, A. (2018). The spread of medical fake news in social media – The pilot quantitative study. Health Policy and Technology, 7(2), 115–118.
• Williams, C. (2007). Research methods. Journal of Business & Economics Research (JBER), 5(3), 78-95. World Health Organization. (2020). Coronavirus disease 2019 (COVID-19): Situation report. Retrieved May 14, 2021 from https://apps.who.int/iris/handle/10665/331686.
• Yanatma, S. (2018). Reuters Institute digital news report 2018. Turkey supplementary report (Reuters Institute for the study of journalism reports). Reutersinstitute.politics.ox.ac.uk. Retrieved May 14, 2021 from https:// reutersinstitute.politics.ox.ac.uk/our-research/digital-news-report-2018-turkey-supplementary-report.
• Yong, A. G. & Pearce, S. (2013). A beginner’s guide to factor analysis: Focusing on exploratory factor analysis.Tutorials in Quantitative Methods for Psychology, 9(2), 79–94.
• Zhou, X. & Zafarani, R. (2020). A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Computing Surveys, 53(5), 1-40.