Dijital Çağda Dezenformasyon: X'te Maymun Çiçeği Virüsü

Yıl 2025, Cilt: 10 Sayı: 23, 304 - 329, 31.01.2025

Büşra Sarıkaya

https://doi.org/10.37679/trta.1564114

Öz

Yanlış ve sahte haberler, toplumsal yapıların sağlıklı işleyişini tarih boyunca engellemiştir. Siyasi, ekonomik ve sağlık alanındaki yanlış veya yanıltıcı haberlerin kısa sürede toplumsal infial yaratabildiği bilinmektedir. Dijitalleşme ve sosyal medyanın yaygınlaşması, sahte haberlerin hızla yayılmasını kolaylaştırırken gerçek haberlerle ayrımını zorlaştırmıştır. Özellikle sağlık alanındaki dezenformasyon, Kovid-19 pandemisi sırasında aşı karşıtı hareketlerin ve komplo teorilerinin radikalleşmesine neden olmuştur. Bu süreçte, yanlış bilgilerin yayılması halk sağlığı önlemleri için ciddi bir sorun oluşturmuştur. Bireysel düzeyde, düşük sağlık okuryazarlığı, bilgi eksiklikleri ve duygusal motivasyon yanlış bilgilere karşı savunmasızlığı artırmaktadır. Sosyal düzeyde ise bilgi kaynaklarının güvenilirliği ve sosyal normlar, yanlış bilgilerin paylaşılmasında etkili olmaktadır. Ayrıca statü arayışı ve grup aidiyeti ihtiyacı bu süreci pekiştirmektedir. Bağlamsal düzeyde ise duygular ve mesajların tekrarı, yanlış bilgilere inanma ve paylaşma davranışlarını tetiklemektedir. Yanlış bilgileri önlemeye yönelik müdahaleler, bireysel bilgi ve beceri geliştirme stratejilerini, sosyal normları yeniden şekillendirmeyi ve sosyal medya platformlarının düzenlenmesini içermektedir. Ayrıca, sosyal eşitsizliklerin bu süreçteki rolü göz önünde bulundurulmalıdır. Yanlış bilgiye duyarlılığın bağlama bağlı olup olmadığı netleşmemiştir; bu nedenle, müdahalelerin çok boyutlu faktörleri içermesi gerekmektedir. Bu çalışmada, maymun çiçeği virüsü hakkında X platformu üzerinden yayın yapan bireysel hesapların içerikleri içerik analizi yöntemiyle ele alınacaktır. İçerik analizi, yazılı, görsel ve işitsel materyalleri sistematik ve nesnel bir şekilde incelemeye olanak tanımaktadır. Bu yöntem, dezenformasyonun biçimsel ve tematik özelliklerini tespit etmek, kullanılan dilin yapısını analiz etmek ve yayılım dinamiklerini anlamak için kapsamlı bir çerçeve sunmaktadır. Buradaki amaç, bilinçli şekilde yanlış haberlerin dijital medya platformlarında hızlıca yayılabilir olmasını tespit etmektir. Bunun yanı sıra dezenformasyonların özellikle sağlık haberleri üzerinde bireysel, sosyal ve durumsal-bağlamsal düzeydeki inanç ve paylaşım süreçleri üzerindeki etkileyici faktörler incelenecektir.

Anahtar Kelimeler

Dezenformasyon, Sosyal Medya, Sağlık Haberleri, Gazetecilik, Sağlık İletişimi

DISINFORMATION IN THE DIGITAL AGE: APPROACHES TO REDUCING RISKS IN DIGITAL HEALTH COMMUNICATION

Öz

Disinformation and fake news have long undermined societal cohesion. Inaccurate or misleading information, especially regarding politics, economics, or health, can trigger social unrest. Technological advancements and the rise of digital platforms, particularly social media, have amplified the spread of false information, complicating the distinction between genuine and fabricated news. Health-related misinformation, notably during the COVID-19 pandemic, posed significant public health challenges, contributing to the rise of anti-vaccine sentiments and conspiracy theories. On an individual level, susceptibility to misinformation is influenced by knowledge deficits, limited health literacy, and emotional motivations. Social factors, such as the credibility of information sources and group dynamics, also contribute to the spread of false information. Furthermore, contextual elements like emotional appeals and message repetition play a crucial role in the belief and dissemination of misinformation. Interventions at the individual level emphasize the enhancement of knowledge and critical skills, while social-level strategies focus on reshaping social norms. Additionally, regulating social media platforms is seen as key to curbing the spread of false information. This study explores the individual, social, and contextual factors that influence the belief in and sharing of misinformation, particularly in the realm of health news, and discusses potential interventions.

Anahtar Kelimeler

Disinformation, digital age, social media, journalism, health news

Kaynakça

• Acerbi, A., Altay, S., Mercier, H., 2022. Research note: Fighting misinformation or fighting for information? Harvard Kennedy School Misinformation Review, 3(1), 1–15. https://doi.org/10.37016/mr-2020-87
• Ackerknecht, E. H., 2016. A short history of medicine. Johns Hopkins University Press.
• Arora, V. M., Rousseau, D., Schwitzer, G., 2019. Why bolstering trust in journalism could help strengthen trust in medicine. JAMA, 321(22), 2159. https:// doi.org/10.1001/jama.2019.0636.
• Austin, E. W., Borah, P., Domgaard, S., 2021. Kovid-19 disinformation and political engagement among communities of color: The role of media literacy. Harvard Kennedy School Misinformation Review, 1, 1–15. https://doi. org/10.37016/mr-2020-58
• Agley, J., Xiao, Y., 2021. Misinformation about Kovid-19: Evidence for differential latent profiles and a strong association with trust in science. BMC Public Health, 21(1), 1–12. https://doi.org/10.1186/s12889-020-10103-x
• Bago B, Rand D. G., Pennycook, G., 2022. Does deliberation decrease belief in conspiracies? J Exp SocPsycho. l103:104395.
• Brashier NM, Schacter D.L., 2020. Aging in an era of fake news. CurrDirPsychol Sci 29:316–323
• Butler L, Fay, N., Ecker, U., 2022. Social endorsement influences the continued belief in corrected misinformation https://doi.org/10.31234/osf.io/ 3fv4d
• Chomsky, N., Herman, E. S., 1988. Manufacturing Consent: The Political Economy of the Mass Media. Pantheon Books
• Cinelli M, Quattrociocchi, W., Galeazzi, A., 2020. The Kovid-19 social media infodemic. Sci Rep 10:16598.
• Ecker, U. K., Lewandowsky, S., Cook, J., Schmid, P., Fazio, L. K., Brashier, N., Kendeou, P., Vraga, E. K., Amazeen, M. A., 2022. The psychological drivers of misinformation belief and its resistance to correction. Nature Reviews Psychology, 1(1), 13–29. https://doi.org/10.1038/s44159-021-00006-y.
• Freiling I, Krause N. M., Scheufele, D. A., Brossard D., 2023. Believing and sharing misinformation, factchecks, and accurate information on social media: the role of anxiety during Kovid-19. New Media Soc. 25:141–162.
• Gilbert, D. T., 1991. How Mental Systems Believe. American Psychologist, 26(2), s.107-119.
• Globig, L. K., Holtz, N., Sharot, T., 2022. Changing the incentive structure of social media platforms to halt the spread of misinformation. PsyArXiv. https://doi.org/10.31234/osf.io/26j8w
• Gollust, S. E., Fowler, E. F., Niederdeppe, J., 2019. Television news coverage of public health issues and implications for public health policy and practice. Annual Review of Public Health, 40(1), 167–185. https://doi.org/10.1146/ annurev-publhealth-040218- 044017
• Gorwa, R., Binns, R., Katzenbach, C., 2020. Algorithmic content moderation: Technical and political challenges in the automation of platform governance. Big Data & Society, 7(1), 1–15. http://dx.doi.org/10.1177/2053951719897945.
• Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., Lazer, D., 2019. Fake news on Twitter during the 2016 US presidential election. Science, 363(6425), 374–378. https://doi.org/10.1126/science.aau2706.
• Guess A. M., Malhotra, N., Pan, J., 2023. How do social media feed algorithms affect attitudes and behavior in an election campaign? Science 381:398–404
• Horne, Benjamin D., Adalı, S., 2017. This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News. https://www.researchgate.net/publication/315682536_This_ Just_In_Fake_News_Packs_a_Lot_in_Title_Uses_Simpler_Repetitive_Content_in_Text_Body_More_Similar_to_Satire_than_Real_News, çevrim içi 10.09.2024.
• Hsu, T., Thompson, S. A., 2023. Chatbots and AI are helping spread misinformation online. The New York Times. https://www.nytimes.com/2023/02/08/technology/ai-chatbots-disinformation.html, çevrim içi 15.09.2024
• Kohring, M., Zimmermann, F., 2020. Aktuelle Desinformation: Definition – Konsequenzen – Gegenmassnahmen, Was ist Desinformation? Betrachtungen aus sechs wissenschaftlichen Perspektiven içinde, Düsseldorf, Landesanstalt für Medien NRW.
• Krippendorff, K., 2013. Content Analysis: An Introduction to Its Methodology. SAGE Publications
• Krishnan, N., Gu, J., Tromble, R., Abroms, L. C., 2021. Research note: Examining how various social media platforms have responded to Kovid-19 misinformation. Harvard Kennedy School Misinformation Review, 2(6), 1–25. https://doi.org/10.37016/mr-2020-85.
• Li, W., Watts, J., Tan, N., 2019. From screen to screening: Entertainment and news television media effects on cancer screening behaviors. Journal of Health Communication, 24(4), 385–394. https://doi.org/10.1080/10810730.2019. 1607954
• Martel, C., Pennycook, G., Rand, DG., 2020. Reliance One Motion Promotes Belief in Fake News. Cognitive Research: Principles and Implications, 5(47). https://doi.org/10.1186/s41235-020-00252-3.
• Mayring, P., 2014. Qualitative Content Analysis: Theoretical Foundation, Basic Procedures and Software Solution. Beltz.
• Motta, M., Callaghan, T., 2020. The pervasiveness and policy consequences of medical folk wisdom in the U.S. Scientific Reports, 10(1), 10722. https://doi.org/10.1038/s41598-020- 67744-6.
• Nadarevic, L., Reber, R., Helmecke AJ., Köse, D., 2020. Perceived truth of statements and simulated social media postings: an experimental investigation of source credibility, repeated exposure, and presentation format. Cogn Res Princ Implic.
• Nan, X., Wang, Y., & Thier, K., 2021. Health Misinformation. In The Routledge Handbook of Health Communication (3rd ed., pp. 318–332). Routledge. http://dx.doi.org/10.4324/9781003043379-27.
• Neuendorf, K. A., 2017. The Content Analysis Guidebook. SAGE Publications.
• Oxman, M., Larun, L., Gaxiola, G. P., Alsaid, D., Qasim, A., Rose, C. J., Bischoff, K., Oxman, A. D., 2022. Quality of information in news media reports about the effects of health interventions: Systematic review and meta-analyses. F1000 Research, 45. https://doi.org/10.12688/f1000research.52894.2.
• Pennycook, G., Rand, DG., 2019. Lazy, not biased: susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning.Cognition.
• Pennycook G., Rand, DG., 2020. Who falls for fake news? The roles of bullshit receptivity, overclaiming, familiarity, and analytic thinking. JPers
• Pennycook, G., Rand, D. G. 2020. Who falls for fake news? The roles of bullshit receptivity, overclaiming, familiarity, and analytic thinking. Journal of Personality, 88(2), 185–200. https://doi.org/10.1111/jopy.12476.
• Pennycook, G., McPhetres, J., Zhang, Y., Lu, JG., Rand, DG., 2020. Fighting Kovid-19 misinformation on social media: experimental evidence for a scalable accuracy-nudge intervention. Psychol Sci 31:770– 780
• Pennycook, G., Rand, D. G.,2022. Accuracy prompts are a replicable and generalizable approach for reducing the spread of misinformation. Nature Communications, 13(1), 1- 12. https://doi.org/10.1038/s41467-022-30073-5.
• Pickles K., Cvejic E., Nickel B., 2021. Kovid-19 misinformation trends in Australia: prospective longitudinal national survey. J Med Internet Res.
• Sarıkaya, B. F., 2023. Z-Kuşağı İnfluencer Olmak İstiyor Mu? Türk ve Alman Gençleri Üzerine Bir Çalışma. Eğitim Yayınevi, Konya
• Scherer, L. D., Pennycook, G., 2020. Who is susceptible to online health misinformation? Am J Public Health.
• Scherer, L. D., McPhetres, J., Pennycook, G., Kempe, A., Allen, L. A., Knoepke, C. E., Tate, C. E., & Matlock, D. D. 2021. Who is susceptible to online health misinformation? A test of four psychosocial hypotheses. Health Psychology. https://doi.org/10.1037/hea0000978
• Schültz, B., Jones, Ch., 2024. Falsch- und Desinformation in sozialen Medien: Ansätze zur Minimierung von Risiken in digitaler Kommunikation über Gesundheit
• Schwarz, N. 2018. Of fluency, beauty, and truth: Inferences from meta cognitive experiences. In J. Proust & M. Fortier (Eds.), Metacognitive diversity: An interdisciplinary approach (pp. 25–46). Oxford University Press.
• Shao, C., Hui, P-M., Wang, L., 2018. Anatomy of an online Misinformation Network. PLoS One
• Sternisko, A., Cichocka, A., Cislak, A., & Van Bavel, J. J., 2021. National narcissism predicts the belief in and the dissemination of conspiracy theories during the Kovid-19 pandemic: Evidence from 56 countries. Personality and Social Psychology Bulletin, https://doi.org/10.1177/01461672211054947
• Uscinski, J. E., Enders, A. M., Klofstad, C., Seelig, M., Funchion, J., Everett, C., Wuchty, S., Premaratne, K., Murthi, M., 2020. Why do people believe Kovid-19 conspiracy theories? Harvard Kennedy School Misinformation Review, 1(3), 1–12. https://doi.org/10.37016/mr-2020-015.
• Vincent, J., 2020. Twitter is bringing its ‘read before you retweet’ prompt to all users. The Verge. https://www.theverge.com/2020/9/25/21455635/twitter-read-beforeyou-tweet-article-prompt-rolling-out-globally-soo, çevrim içi 20.08.2024.
• Vosoughi, S., Roy, D., Aral, S. 2017. The spread of true and false news online. Science 359:1146–1151
• Wang, Y., McKee, M., Torbica, A., Stuckler D., 2019. Systematic Literature Review on the Spread of Health-related Misinformation on Social Media. Soc Sci Med. Nov; 240:112552. doi: 10.1016/j.socscimed.2019.112552. Epub 2019 Sep 18. PMID: 31561111; PMCID: PMC7117034.
• Zarocostas, J., 2020. How to fight an infodemic. Lancet 395:676.
• Zollo, F., Bessi, A., Vicario, M.D., 2017. Debunking in a World of tribes. PLoS ONE.