COVID-19 Aşılaması İle İlgili Türkiye’de Yanlış Enformasyon: Twitter Paylaşımları Üzerine Analiz

Yıl 2022, , 89 - 104, 07.12.2022

Beris Artan Özoran , Ilgar Seyıdov

https://doi.org/10.31123/akil.1171653

Öz

COVID-19 pandemisi tüm dünyayı toplumsal, kültürel, ekonomik ve siyasal olarak derinden etkilemiştir. Pandeminin etkileri ve sonuçları ile mücadele ülkelerin odak noktası haline gelmiştir. Süreç içinde farklı tedavi yöntemleri ve çalışmalar yürütülüyorken, mevcut aşılanma ile ilgili tartışmalar da devam etmektedir. Son istatistik bilgilere göre dünya nüfusunun 63%’ü iki doz aşılanmış durumdadır. Özellikle aşılarla ile ilgili bilgi kirliliği bu sürecin yavaşlamasına neden olmakta ve daha önemlisi aşılanmamadan dolayı virüsün çeşitli mutasyonlarının da ortaya çıkmasına neden olmaktadır. Bu anlamda bu çalışma dünyadan en fazla vaka sayısında ve orta ölçekli aşılanma düzeyine sahip Türkiye’de aşılanmama ile ilgili Twitter’da yayınlanan yanlış bilgileri incelemeyi amaçlamaktadır. Çalışmada iki aşamalı yöntem izlenilmiştir. Öncelikle aşılarla ilgili paylaşımlar Twitter API aracılığıyla veri toplanılmış, Orange Data Mining Programı ile analiz gerçekleştirilmiş, sonra ise içerik analizi uygulanmıştır. Bulgulara göre propaganda en fazla öne çıkan yanlış enformasyon türü olurken, “pandeminin varlığının inkârı” en fazla değinilen konu olmuştur. Diğer taraftan “PCR testlerini durdurun” ve “pandemi bitti” en çok vurgulanan diğer söylemler olmuştur.

Anahtar Kelimeler

Yanlış enformasyon, Covid-19, Aşılanma, Türkiye, Twitter.

False Information about COVID-19 Vaccination in Turkey: Analysis of Twitter Posts

Öz

The COVID-19 pandemic has affected the world socially, culturally, economically, and politically. Struggling with the COVID-19 virus has become the focal point of the countries. As many studies are being conducted, and new treatment methods are being discussed, the vaccination process continues worldwide. According to the current statistics, 63% of the world population has been already fully vaccinated. During this period, along with the true information, many false information facts and materials proliferated which lead to the reluctance of individuals to be vaccinated. As a result of
it, the virus exposes to mutation and more serious cases emerge worldwide. In this context, this study aims to analyze false information Tweets regarding vaccination in Turkey. As Turkey is one of the top countries with the highest cases and the medium-scaled (68%) level of vaccination worldwide, the study findings will help to understand the main motives of anti-vaccination by focusing on false facts. A two-step methodology was followed in the research. First, data collection was done through Twitter API and then, the analysis was conducted using the Orange Data Mining Program and content analysis. Propaganda is one of the interesting results as the most-shared false information type. On the other hand, while “the denial of the epidemic” was the most-focused theme, “stop insisting on PCR” and “pandemic is over” were the most-emphasized discourses in the Tweets.

Anahtar Kelimeler

False information, Covid-19, Vaccination, Turkey, Twitter.

Kaynakça

• Akyüz, S. S. (2020). Yanlış Bilgi Salgını: COVID-19 Salgını Döneminde Türkiye’de Dolaşıma Giren Sahte Haberler. Akdeniz Üniversitesi İletişim Fakültesi Dergisi, 34, 422-444. https://doi.org/10.31123/akil.779920.
• Anggraini, N. & Suroyo, H. (2019). Comparison of Sentiment Analysis against Digital Payment “T-cash and Go-pay” in Social Media Using Orange Data Mining. Journal of Information Systems and Informatics, 1(2), 152-163.
• Aydın, A. F. (2020). Post-Truth Dönemde Sosyal Medyada Dezenformasyon: Covid-19 (Yeni Koronavirüs) Pandemi Süreci. Asya Studies-Academic Social Studies/Akademik Sosyal Araştırmalar, 4(12), 76-90. https://doi.org/10.31455/asya.740420.
• Bashir, S., Bano, S., Shueb, S., Gul, S., Mir, A. A., Ashraf, R. & Noor, N. (2021). Twitter chirps for Syrian people: Sentiment analysis of tweets related to Syria Chemical Attack. International Journal of Disaster Risk Reduction, 62, 1-10. https://doi.org/10.1016/j.ijdrr.2021.102397.
• Casillano, J. A. B. & Casillano, N. F. B. (2021). Newnormal: Understanding Public Tweets on Living In The New Normal. EPRA International Journal of Multidisciplinary Research (IJMR), 7(12), 1-1.
• Demšar, J., Zupan, B., Leban, G. & Curk, T. (2004). Orange: From experimental machine learning to interactive data mining. In. J. F. Boulicaut, F. Esposito, F. Giannotti & D. Pedreschi (Eds.), European conference on principles of data mining and knowledge discovery (pp. 537-539). Berlin: Springer.
• Dib, F., Mayaud, F., Chauvin, P. & Launay, O. (2021). Online Mis/Disinformation and Vaccine Hesitancy In The Era Of COVID-19: Why We Need An Ehealth Literacy Revolution. Human Vaccines & Immunotherapeutics, 24, 1-3. https://doi.org/10.1080/21645515.2021.1874218.
• Feltzer, H. J. (2004). Disinformation: The Use of False Information. Minds and Machines, 14, 231-240.
• Floridi, L. (1996). Brave.net World: The Internet as a Disinformation Superhighway?. The Electronic Library, 14(5), 509-514.
• Floridi, L. (2010). Semantic Information and The Correctness Theory of Truth. Erkenntnis, 74(2), 1-29. http://dx.doi.org/10.1007/s10670-010-9249-8.
• Galhardi, C. P., Frieire, P, N., Minayo, S, C, M. & Fagundes, F. C. M. (2021). Fact or Fake? An analysis of disinformation regarding the Covid-19 pandemic in Brazil. Ciência & Saúde Coletiva, 25(2), 4201-4210. https://doi.org/10.1590/1413-812320202510.2.28922020.
• Giglietto, F., Iannelli, L., Rossi, L. & Valeriani, A. (2016). Fakes, news and the election: A new taxonomy for the study of misleading information within the hybrid media system. Urbino: SSRN.
• Giglietto, F., Iannelli, L., Valeriani, A. & Rossi, L. (2019). Fake News is the Invention of a Liar: How False Information Circulates within the Hybrid News System. Current Sociology Management, 67(4), 625-642. https://doi.org/10.1177%2F0011392119837536.
• Gottlieb, M. & Dyer, S. (2020). Information and Disinformation: Social Media in the COVID-19 Crisis. Academic emergency medicine: official journal of the Society for Academic Emergency Medicine, 27(7), 640–641. https://doi.org/10.1111/acem.14036.
• Guo, B., Ding, Y., Yoo, L., Liang, Y. & Yu, Z. (2020). The Future of False Information Detection on Social Media: New Perspectives and Trends. ACM Comput. Surv., 53(4), 1-36. https://doi.org/10.1145/3393880.
• Jack, C. (2019). Wicked Content. Communication, Culture & Critique, 12(4), 435-454.
• Kadenko N. I., van der Boon J. M., van der Kaaij J., Kobes W. J., Mulder A.T. & Sonneveld J. J. (2021). Whose Agenda Is It Anyway?: The Effect of Disinformation on COVID-19 Vaccination Hesitancy in the Netherlands. In: N. Edelmann et al. (Ed.), Electronic Participation. ePart 2021. Lecture Notes in Computer Science (pp. 55-65). USA: Springer.
• Karakaş, O. & Doğru, Y. B. (2021). Covid-19 Aşılarına Yönelik Üretilen Yeni Medya İçeriklerinin PostTruth Kavramı Bağlamında Analizi. Asya Studies-Academic Social Studies / Akademik Sosyal Araştırmalar, 5(16), 163-182. https://doi.org/10.31455/asya.878400.
• Kearney, M. D., Chiang, S. C. & Massey, P. M. (2020). The Twitter origins and evolution of the COVID-19 “plandemic” conspiracy theory. Harvard Kennedy School Misinformation Review, 1(3), 1 -18.
• Koca, G. (2021). Bıtcoın Üzerine Twitter Verileri ile Duygu Analizi. Anadolu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 22(4), 19-30. https://doi.org/10.53443/anadoluibfd.988262.
• Kumar, S., Wets, R. & Leskovec, J. (2016, 11-15 April). Disinformation on the Web: Impact, Characteristics and Detection of Wikipedia Hoaxes. Proceedings of the 25th International Conference on World Wide Web (pp.591-602), 11-15 April 2016, Montreal, Quebec, Canada.
• Kumar, S. & Shah, N. (2018). False information on web and social media: A survey. Arxiv Preprint, 1(1), 1-35.
• Montagni, I., Ouazzani-Touhami, K., Mebarki, A., Texier, N., Schück, S., Tzourio, C. & CONFIN group (2021). Acceptance of a Covid-19 vaccine is associated with ability to detect fake news and health literacy. Journal of public health, 43(4), 695-702. https://doi.org/10.1093/pubmed/fdab028.
• Moran, P. (2020). Social Media: A Pandemic of Misinformation. The American Journal of Medicine, 133(11), 1247–1248. https://doi.org/10.1016%2Fj.amjmed.2020.05.021.
• Nguyen, A. & Catalan, D. (2020). Digital Mis/Disinformation and Public Engagement with Health and Science Controversies: Fresh Perspectives from Covid-19. Media and Communication, 8(2), 323-328. https://doi.org/10.17645/mac.v8i2.3352.
• Opesade, A. O. (2021). Twitter-Mediated Enterprise–Customer Communication: Case of Electricity Distribution Services in a Developing Country. Social Science Computer Review, 1, 1-17. https://doi.org/10.1177/08944393211019571.
• Petit J, L. C., Millet, B., Ali, K., & Sun, R. (2021). Can We Stop the Spread of False Information on Vaccination? How Online Comments on Vaccination News Affect Readers’ Credibility Assessments and Sharing Behaviors. Science Communication, 43(4), 407-434. https://doi.org/10.1177/10755470211009887.
• Pierri, F. & Ceri, S. (2019). False News On Social Media: A Data-Driven Survey. SIGMOD Record, 48(2), 18-32.
• Porsuk, Ö. A. & Cerit, Ç. (2021). Sosyal Medyada Covid-19 Aşisi Tartişmalari: Ekşi Sözlük Örneği. Hacettepe Sağlık İdaresi Dergisi, 24(2), 347-360.
• Scarantino, A. & Piccinini, G. (2010). Information without truth. Metaphilosophy, 41(3), 313-330.
• Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M. & Liu, Y. (2019). Combating Fake News: A Survey on Identification and Mitigation Techniques. ACM Transactions Intelligent Systems and Technology, 10(3), 1-41. https://doi.org/10.1145/1122445.1122456.
• Shu, K., Bhattacharjee, A., Alatawi, F., Nazer, H.T., Ding, K., Karami, M. & Liu, H. (2020). Combating Disinformation in Social Media Age. WIREs Data Mining and Knowledge Discovery, 10(6), 1-23. https://doi.org/10.1002/widm.1385.
• Soe, O. S. (2018). Algorithmic Detection of Misinformation and Disinformation: Gricean Perspectives. Journal of Documentation, 74(2), 309-322. https://doi.org/10.1108/JD-05-2017-0075.
• Sismondo, S. (2017). Post-Truth?. Social Studies of Science, 47(1), 3-6.https://doi.org/10.1177/0306312717692076.
• Tagliabue, F., Galassi, L. & Mariani, P. (2020). The “Pandemic” of Disinformation in COVID-19. SN Compr. Clin. Med., 2, 1287–1289. https://doi.org/10.1007/s42399-020-00439-1.
• Topsakal, T. (2021). Dijital ortamda yanlış bilgi ve haberlerin yayılması: Koronavirüs salgın haberlerine dair bir inceleme. İNİF E- Dergi, 6(1), 382-400. https://doi.org/10.47107/inifedergi.867934.
• Wilson, S.L. & Wiysonge, C. (2020). Social media and vaccine hesitancy. BMJ Global Health,5, 1-7. https://doi. org/10.1136/.
• Zannettou, S., Sirivianos, M., Blackburn, J. & Kourtellis. N. (2019). The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans. J. Data and Information Quality, 11(3), 1-26. https://doi.org/10.1145/3309699.
• Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M. & Procter, R. (2018). Detection and resolution of rumours in social media: A survey. ACM Computing. Survey, 51(2), 32-36. https://doi.org/10.1145/3161603.
• BBC (2021). “Covid Aşı Haritası”. Retrieved June 25, 2021, from https://www.bbc.com/turkce/haberler-dunya-56025069.
• CDC (2022). “CDC Museum Covid-19 Timeline”, Retrieved September 25, 2022, from https://www.cdc.gov/museum/timeline/covid19.html.
• Cook, J., Der Linden, V. S., Lewandowsky, S. and Ecker, H. K. U. (2020). “Coronavirus, ‘Plandemic’ and the seven traits of conspiratorial thinking”. Retrieved October 23, 2022, from https://research-information.bris.ac.uk/en/publications/coronavirus-plandemic-and-the-seven-traits-of-conspiratorial-thin.
• Fallis, D. (2009). “A Conceptual Analysis of Disinformation”. Retrieved Janurary 25, 2021, from https://www.ideals.illinois.edu/bitstream/handle/2142/15205/fallis_disinfo1.pdf?sequence=2&isAllowed=y.
• Marwick, A. & Lewis, R. (2017). “Media Manipulation and Disinformation Online”. Retrieved October 23, 2022, from https://datasociety.net/library/media-manipulation-and-disinfo-online/.
• Our World in Data (2022). “Coronovirus Vaccinations”. Retrieved September 25, 2022, from https://ourworldindata.org/covid-vaccinations.
• Twitter (2022). “Covid-19 Misleading Information Policy”. Retrieved Februrary 10, 2021, from https://help.twitter.com/en/rules-and-policies/medical-misinformation-policy.
• WHO (2020). “Archived: WHO Timeline-Covid-19”. Retrieved October 25, 2922, from https://www.who.int/news/item/27-04-2020-who-timeline---covid-19.
• Worldometers (2022). “Coronavirus Cases”. Retrieved Februrary 10, 2021, from https://www.worldometers.info/corona-virus/.