Kovid-19 Hakkında Komplo Teorisi İçeren Dijital İçeriklerin Biçimsel Özellikleri ve Yayılım Düzeyleri Arasındaki İlişkiye Yönelik Bir Analiz

Yıl 2022, , 8 - 27, 30.01.2022

Oğuz Kuş

https://doi.org/10.37679/trta.1013649

Öz

Kovid-19 aşıları hakkında komplo teorilerinin sosyal ağlarda dolaşıma girdiği bilinmektedir. Bu çalışmada kovid-19 bağlamında aşı karşıtı komplo teorilerini içeren tweetler analiz edilmiş ve tweetlerin biçimsel özellikleri ile yayılım düzeyleri arasındaki ilişki sorgulanmıştır. #SalgınYalanAşıOlmuyorum hashtaginden toplanan 1113 tweetin biçimsel özellikleri nicel içerik analizi ile çözümlenmiş ve hipotezleri test etmek üzere Ki-kare testi uygulanmıştır. Yüksek karakter sayısı kullanım düzeyi olan tweetlerin yüksek düzeyde yayılım gösteren tweetler arasındaki payının yüksek olduğu bulunmuştur. Ayrıca bulgular düşük hashtag kullanım düzeyi ve düşük kişi etiketi kullanım düzeyine sahip tweetlerin yüksek düzeyde yayılım gösteren tweetler arasındaki payının yüksek olduğuna işaret etmektedir. Aşı karşıtı tweetlerdeki bu biçimsel özellikleri anlamak aşı kabulünü artırabilecek nitelikli bilgilerin Tweetosphere’de çoğaltılabilmesi ve bu nitelikli içeriğin etkisinin artırılabilmesi için pratiğe yönelik bir önem taşımaktadır. Diğer yandan, içeriğin biçimsel boyutunun yayılımla ilişkisine ışık tutarak gelecek araştırmalarda göz önünde bulundurulabilecek yeni değişkenler sunmaktadır. Bu durum, aşı karşıtlığı ve komplo teorileri bağlamındaki araştırmaların derinleşebilmesi ve yeni çözüm önerileri sunabilmesi için bir potansiyel sunmaktadır.

Anahtar Kelimeler

Yeni medya, Sosyal Medya, COVID-19, İnfodemi, Komplo Teorileri

An Analysis on the Relationship Between Formal Characteristics and Diffusion Levels of Digital Content Containing Conspiracy Theories About COVID-19

Öz

It is known that conspiracy theories about COVID-19 vaccines are circulating on social networks. In this study, tweets containing anti-vaccine conspiracy theories in the context of COVID-19 were analyzed and the relationship between formal characteristics of tweets and their diffusion levels was questioned. Formal features of 1113 tweets collected from the hashtag #SalgınYalanAşıOlmuyorum (#PandemicIsLieIDontGetVaccinated) were analyzed via quantitative content analysis and the Chi-square test was applied to test the hypotheses. It was found that the share of tweets with high character count usage level among tweets with high diffusion level is high. In addition, the share of tweets with low hashtag usage level and low person tag usage level among tweets with high diffusion level is high. Understanding the formal features of anti-vaccine tweets is of practical importance for producing high quality information that can increase vaccine acceptance in Tweetosphere and to increase the impact of this high quality content. On the other hand, it sheds light on the relationship between the formal dimension of content and diffusion, and offers new variables that can be considered in future research. This presents a potential for research in the context of anti-vaccine and conspiracy theories to deepen and offer new solutions.

Anahtar Kelimeler

New Media, Social Media, Conspiracy Theories, COVID-19, Infodemic

Kaynakça

• Abedin, B., Babar, A., & Abbasi, A. (2014, December). Characterization of the use of social media in natural disasters: a systematic review. In 2014 IEEE Fourth International Conference on Big Data and Cloud Computing (pp. 449-454). IEEE.
• Akyüz, S. S. (2021). Aşı Karşıtlığı ve Şeffaflık Algısında İletişim Pratikleri ve Siyasal Aidiyetlerin Rolü. Yeni Medya Elektronik Dergisi, 5(2), 172-185.
• Allington, D., Duffy, B., Wessely, S., Dhavan, N. ve Rubin, J. (2020). Health-protective behaviour, social media usage and conspiracy belief during the COVID-19 public health emergency. Psychological Medicine, 1-7.
• Berger, J., & Milkman, K. L. (2012). What makes online content viral?. Journal of marketing research, 49(2), 192-205.
• Bierwiaczonek, K., Kunst, J. R., & Pich, O. (2020). Belief in COVID‐19 conspiracy theories reduces social distancing over time. Applied Psychology: Health and Well‐Being, 12(4), 1270-1285.
• Chong, M. (2019). Discovering fake news embedded in the opposing hashtag activism networks on Twitter:# Gunreformnow vs.# NRA. Open Information Science, 3(1), 137-153.
• Deborah Agostino, Michela Arnaboldi & Melisa Diaz Lema (2021) New development: COVID-19 as an accelerator of digital transformation in public service delivery, Public Money & Management, 41:1, 69-72, DOI: 10.1080/09540962.2020.1764206
• Douglas, K. M. (2021). COVID-19 conspiracy theories. Group Processes & Intergroup Relations, 24(2), 270-275.
• Duplaga, M. ve Grysztar, M. (2021). The Association between Future Anxiety, Health Literacy and the Perception of the COVID-19 Pandemic: A Cross-Sectional Study. Healthcare, 9(1), 43.
• Dünya Sağlık Örgütü (2020). Coronavirus disease (COVID-19) Situation Report – 169. who.int/docs/default-source/coronaviruse/situation-reports/20200707-covid-19-sitrep-169.pdf?sfvrsn=c6c69c88_2 adresinden alındı
• Freeman, D., Loe, B. S., Chadwick, A., Vaccari, C., Waite, F., Rosebrock, L., ... & Lambe, S. (2020). COVID-19 vaccine hesitancy in the UK: the Oxford coronavirus explanations, attitudes, and narratives survey (Oceans) II. Psychological medicine, 1-15.
• Goertzel, T. (1994). Belief in conspiracy theories. Political psychology, 731-742.
• Haslam, C. R., Madsen, S., & Nielsen, J. A. (2021). Crisis-driven digital transformation: Examining the online university triggered by COVID-19. In Digitalization (pp.291-303). Springer, Cham.
• Huang, Y. L., Starbird, K., Orand, M., Stanek, S. A., & Pedersen, H. T. (2015, February). Connected through crisis: Emotional proximity and the spread of misinformation online. In Proceedings of the 18th ACM conference on computer supported cooperative work & social computing (pp. 969-980).
• Ibrahim, N. F., Wang, X., & Bourne, H. (2017). Exploring the effect of user engagement in online brand communities: Evidence from Twitter. Computers in Human Behavior, 72, 321-338.
• Iqbal Khan, S. and Ahmad, B. (2021), "Tweet so good that they can't ignore you! Suggesting posting strategies to micro-celebrities for online engagement", Online Information Review, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/OIR-08-2020-0334
• Jackson, A. M., Mullican, L. A., Yin, J., Tse, Z. T. H., Liang, H., Fu, K. W., ... & Fung, I. C. H. (2018). # CDCGrandRounds and# VitalSigns: a Twitter analysis. Annals of global health, 84(4), 710.
• Jeong, B. G., & Yeo, J. (2018). United Nations and Crisis Management. Global Encyclopedia of Public Administration, Public Policy, and Governance. Cham: Springer International Publishing AG, 6041-6048.
• Kumar, S., Huang, B., Cox, R. A. V., & Carley, K. M. (2021). An anatomical comparison of fake-news and trusted-news sharing pattern on Twitter. Computational and Mathematical Organization Theory, 27(2), 109-133.
• Maryland State Department of Education (2013). Text Complexity Clarification and Resource Guide. https://www.stevenson.edu/academics/schools/school-sciences/stem-initiatives/project-lead-the-way/documents/Text-Complexity-Clarification-and-Resource-Guide.pdf adresinden alındı.
• Mozdeh Big Data Text Analysis (2020). mozdeh.wlv.ac.uk adresinden alındı.
• Nagel, L. (2020), "The influence of the COVID-19 pandemic on the digital transformationof work", International Journal of Sociology and Social Policy, Vol. 40 No. 9/10, pp. 861-875. https://doi.org/10.1108/IJSSP-07-2020-0323.
• Pedro Soto-Acosta (2020) COVID-19 Pandemic: Shifting Digital Transformation to a High-Speed Gear, Information Systems Management, 37(4), 260-266, DOI: 10.1080/10580530.2020.1814461.
• Pummerer, L., Böhm, R., Lilleholt, L., Winter, K., Zettler, I., & Sassenberg, K. (2020). Conspiracy theories and their societal effects during the COVID-19 pandemic. Social Psychological and Personality Science, 19485506211000217.
• Rath, M., Pati, B., & Pattanayak, B. K. (2018). An overview on social networking: design, issues, emerging trends, and security. Social Network Analytics: Computational Research Methods and Techniques, 21.
• Reuter, C., Kaufhold, M. A., Schmid, S., Spielhofer, T., & Hahne, A. S. (2019). The impact of risk cultures: Citizens’ perception of social media use in emergencies across Europe. Technological Forecasting and Social Change, 148(1), 1-17.
• Riffe, D., Lacy, S., Watson, B. R., & Fico, F. (2019). Analyzing media messages: Using quantitative content analysis in research. New York: Routledge
• Sehl, K. (2020). How the Twitter Algorithm Works in 2020 and How to Make it Work for You. https://blog.hootsuite.com/twitter-algorithm/ adresinden alındı.
• Shugars, S., & Beauchamp, N. (2019). Why keep arguing? Predicting engagement in political conversations online. Sage Open, 9(1), 2158244019828850.
• Suh, B., Hong, L., Pirolli, P., & Chi, E. H. (2010, August). Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In 2010 IEEE second international conference on social computing (pp. 177-184). IEEE.
• Starbird, K., & Palen, L. (2011, May). "Voluntweeters" self-organizing by digital volunteers in times of crisis. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1071-1080).
• Teyit.org (2018). Aşı karşıtlığı ve Covid-19. https://teyit.org/dosya-asi-karsitligi-ve-covid-19 adresinden alındı.
• Tsur, O., & Rappoport, A. (2012, February). What's in a hashtag? Content based prediction of the spread of ideas in microblogging communities. In Proceedings of the fifth ACM international conference on Web search and data mining (pp. 643-652).
• Twitter (2020). Platform manipülasyonu ve spam politikası. https://help.twitter.com/tr/rules-and-policies/platform-manipulation adresinden alındı
• Uscinski, J. E., Enders, A. M., Klofstad, C., Seelig, M., Funchion, J., Everett, C., ... & Murthi, M. (2020). Why do people believe COVID-19 conspiracy theories?. Harvard Kennedy School Misinformation Review, 1(3).
• Valencia, S. W., Wixson, K. K., & Pearson, P. D. (2014). Putting text complexity in context: Refocusing on comprehension of complex text. The Elementary School Journal, 115(2), 270-289.
• Wang, R., Liu, W., & Gao, S. (2016). Hashtags and information virality in networked social movement: Examining hashtag co-occurrence patterns. Online Information Review.
• Weigmann, K. (2018). The genesis of a conspiracy theory: Why do people believe in scientific conspiracy theories and how do they spread?. EMBO reports, 19(4), e45935.
• White, M. D., & Marsh, E. E. (2006). Content analysis: A flexible methodology. Library trends, 55(1), 22-45.
• Whittaker, J., Handmer, J., & McLennan, B. (2015). Informal volunteerism in emergencies and disasters: a literature review. Melbourne, Australia: Bushfire and Natural Hazards CRC.
• Żyłka, K. (2018). Shorter or longer tweets? One year with the expanded character limit [analysis]. https://www.sotrender.com/blog/2018/10/shorter-longer-tweets-one-year-expanded-character-limit-analysis adresinden alındı.