Çin’de Üretilen Aşılara Yönelik YouTube Tartışmaları Çerçevesinde Yanlış Bilgi ve Nefret Söylemi İlişkisi Üzerine Bir İnceleme

Öz

COVID-19 pandemisi geleneksel medya ve yeni medyaya belirgin etkilerde bulunmuştur. Bu bağlamda yeni medya parametrelerine bağlı olarak ortaya çıkan infodeminin toplumsal riskler oluşturduğu gözlemlenmiştir. Bu çalışmada COVID-19 sürecinde dijital platformlardaki yanlış bilgi, nefret söylemi ve kullanıcı etkileşimi miktarı arasındaki ilişki Çin’de üretilmiş aşılar bağlamında incelenmiştir. Beş YouTube videosundan 2919 kullanıcı yorumu toplanmış, tümdengelimci nitel içerik analizi yaklaşımıyla çözümlenmiş, ardından Ki-kare testi uygulanmıştır. Yorumlarda yanlış bilgi ve nefret söylemi mevcudiyeti arasında anlamlı bir ilişki tespit edilmiştir. Ayrıca nefret söylemi ve etkileşim arasında da anlamlı bir ilişki olduğu bulgulanmıştır. Aynı anda hem yanlış bilgi hem de nefret söylemi içeren yorumlar incelendiğinde, çoğunlukla virüsün bir proje olduğunu iddia eden yanlış bilgilerle karşılaşılmıştır. Virüsün bir savaş enstrümanı, aşının ise ticari bir araç olduğuna yönelik iddialarda bulunulduğuna rastlanılmıştır. Ayrıca Sincan Uygur Özerk Bölgesi’ndeki durum kapsamında Türk düşmanlığı gibi farklı bağlamlarda “bize karşı onlar” söylemi yaratıldığı gözlemlenmiştir. Yanlış bilginin mevcut olduğu durumlarda yorumlardaki nefret söyleminin yüzde 5,1 daha fazla olduğu tespit edilmiştir. Yorumların tümündeki ortalama beğeni sayısı 3,4 iken, nefret söylemi içeren kullanıcı yorumlarının ortalama beğeni sayısının 6,6 olduğu gözlemlenmiştir. Yanlış bilgi ve nefret söylemi döngüsel bir şekilde birbirini beslemektedir. Sonuç olarak bu durumun yaşanmakta olan sağlık kriziyle mücadeleyi olumsuz etkileme potansiyeli taşıdığı ortaya konmuştur.

Anahtar Kelimeler

• COVID-19
• infodemi
• yanlış bilgi
• nefret söylemi
• COVID-19 aşıları.

An Analysis on the Relationship Between Misinformation and Hate Speech in the Framework of YouTube Discussions About Vaccines Produced in China

Öz

COVID-19 pandemic significantly influenced conventional and new media. In this context, it has been observed that the infodemic that emerges due to parameters of new media poses social risks. This study aims to analyze the relationship between misinformation, hate speech and the amount of user interaction during COVID-19 on digital platforms in the context of vaccines produced in China. 2919 user comments were collected from the five YouTube videos. Comments were analysed with deductive content analysis and Chi-square test was implemented. A significant relationship exists between misinformation existence and hate speech existence. In addition, a significant relationship was found between hate speech existence and amount of user interaction in comments. Content of comments, where both misinformation and hate speech exist, shows that users often claim that the virus is a project, a war instrument or vaccines are commercial instruments. It is observed that a “they versus us” discourse was established in the context of the situation in Xinjiang Uygur Autonomous Region and Turkophobia. It was identified that when misinformation exists in user comments, the existence of hate speech is 5,1% higher. It was observed that the average amount of user interaction of comments that contain hate speech is 6,6 while the average amount of user interaction in overall comments is 3,4. Finally, it is concluded that this situation can potentially impact the fight with ongoing health crisis in a negative way.

Anahtar Kelimeler

• COVID-19
• infodemic
• misinformation
• hate speech
• COVID-19 vaccines

Kaynakça

1. Ahmed, W., Vidal-Alaball, J., Downing, J. & Seguí, F. L. (2020). COVID-19 and the 5G conspiracy theory: Social network analysis of twitter data. Journal of Medical Internet Research, 22(5), 1–9.
2. Akbar, S. Z., Panda, A., Kukreti, D., Meena, A. & Pal, J. (2021). Misinformation as a window into prejudice: COVID-19 and the Information Environment in India. Proc. ACM Hum.-Comput. Interact., 4(CSCW3), 1-28.
3. Akgül, M. (2020). Çevrim içi ortamlarda nefret söylemi: Ekşi Sözlük’te 65 yaş üstü sokağa çıkma yasağı tartışmaları. İletişim Kuram ve Araştırma Dergisi, (51), 57-78.
4. Albadi, N., Kurdi, M. & Mishra, S. (2018). Are they our brothers? analysis and detection of religious hate speech in the Arabic Twittersphere. 26.01.2021 tarihinde nuhaalbadi.com/assets/papers/AreThey.pdf adresinden edinilmiştir.
5. Allington, D., Duffy, B., Wessely, S., Dhavan, N. & Rubin, J. (2020). Health-protective behaviour, social media usage and conspiracy belief during the COVID-19 public health emergency. Psychological Medicine, 51 (10), 1763-17697.
6. Arcila-Calderón, C., Blanco-Herrero, D., Frías-Vázquez, M. & Seoane, F. (2021). Refugees welcome? Online hate speech and sentiments in Twitter in Spain during the reception of the boat aquarius. Sustainability(Switzerland), 13(5), 2728.
7. Arnot, M., Brandl, E., Campbell, O. L. K., Chen, Y., Du, J., Dyble, M., … Zhang, H. (2020). How evolutionary behavioural sciences can help us understand behaviour in a pandemic Evolution, Medicine and Public Health, 2020(1), 264–278.
8. Atehortua, N. A. & Patino, S. (2020). COVID-19, a tale of two pandemics: novel coronavirus and fake news messaging. Health Promotion International, 36(2), 524-534

9. Awal, M. R., Cao, R., Mitrovic, S. & Lee, R. K. W. (2020). On analyzing antisocial behaviors amid covid-19 pandemic. 10.02.2021 tarihinde arXiv preprint arXiv:2007.10712 adresinden edinilmiştir.
10. Breakwell, G. M. & Jaspal, R. (2020). Identity change, uncertainty and mistrust in relation to fear and risk of COVID-19. Journal of Risk Research, 24(3-4), 335-351.
11. Brown, A. (2018). What is so special about online (as compared to offline) hate speech? Ethnicities, 18(3), 297- 326.
12. Carrapico, H. & Farrand, B. (2020). Discursive continuity and change in the time of Covid-19: the case of EU cybersecurity policy. Journal of European Integration, 42(8), 1111–1126.
13. Castrén, L. (2021). Online hate towards Chinese people during the Covid-19 pandemic. 18.02.2021 tarihinde erepo.uef.fi/bitstream/handle/123456789/24779/161.794.4828165259795.pdf?sequence=-1 adresinden edinilmiştir.
14. Guy, J. ( 2020, 4 Mart). East Asian student assaulted in ‘racist’ coronavirus attack in London. 10.03.2021 tarihinde edition.cnn.com/2020/03/03/uk/coronavirus-assault-student-london-scli-intl-gbr/index.html adresinden edinilmiştir.
15. Dirini, İ. & Özsu, G. (2020). Covid-19 pandemi sürecinde sosyal medyada nefret söylemi raporu. Z. Özarslan (Ed.). 10.01.2021 tarihinde ekitap.alternatifbilisim.org/pdf/covid19-nefret-soylemi-raporu.pdf adresinden edinilmiştir.
16. Döring, N. & Mohseni, M. R. (2019). Fail videos and related video comments on YouTube: A case of sexualization of women and gendered hate speech? Communication Research Reports, 36(3), 254-264.
17. Duplaga, M. & 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.
18. Dünya Sağlık Örgütü (2020, 7 Temmuz). Coronavirus disease (COVID-19) Situation Report – 169. 2.02.2021 tarihinde who.int/docs/default-source/coronaviruse/situation-reports/20200707-covid-19-sitrep-169. pdf?sfvrsn=c6c69c88_2 adresinden edinilmiştir.
19. El-Gilany, A. H. (2020). Infodemics of COVID-19 pandemic. Türkiye Halk Sağlığı Dergisi. 18 (Special issue), 86–95.
20. Elo, S. & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107- 115.
21. Ersoy, N. P. (2020). Binark: Medyada Çinlilere dönük bir nefret söylemi var. 25.02.2021 tarihinde https://www. gazeteruzgarli.com/binark-medyada-cinlilere-donuk-bir-nefret-soylemi-var/ adresinden edinilmiştir.
22. Evanega, S., Lynas, M., Adams, J. & Smolenyak, K. (2020). Coronavirus misinformation: quantifying sources and themes in the COVID-19 “infodemic.” JMIR Preprints, 19(10), 1–13. 10.02.2021 tarihinde allianceforscience.cornell.edu/wp-content/uploads/2020/10/Evanega-et-al-Coronavirusmisinformation- submitted_07_23_20-1.pdf adresinden edinilmiştir.
23. Evolvi, G. (2019). #Islamexit: inter-group antagonism on Twitter. Information Communication and Society, 22(3),386-401.
24. Camacho, M. M. (2020). Learning about reputational risk in the era of covid-19: Disinformation as corporate risk. Doxa Comunicacion, 2020(31), 19–40.
25. Fan, L., Yu, H. & Yin, Z. (2020). Stigmatization in social media: Documenting and analyzing hate speech for COVID ‐19 on Twitter. Proceedings of the Association for Information Science and Technology, 57(1), 1–11.
26. Fortuna, P., & Nunes, S. (2018). A survey on automatic detection of hate speech in text. ACM Computing Surveys (CSUR), 51(4), 1-30.
27. Googman, J. & Carmichael, F. (2020, 27 June). Coronavirüs: 5G and microchip conspiracies around the World. 02.02.2021 tarihinde https://www.bbc.com/news/53191523 adresinden edinilmiştir.
28. Gover, A. R., Harper, S. B. & Langton, L. (2020). Anti-Asian hate crime during the COVID-19 Pandemic: Exploring the reproduction of ınequality. American Journal of Criminal Justice, 45(4), 647-667.
29. Göregenli, M. (2013). Nefret söylemi ve nefret suçları. M. Çınar (Der.), Medya ve nefret söylemi kavramlar, mecralar, tartışmalar (ss.57-73) içinde. İstanbul: Hrant Dink Vakfı Yayınları.
30. Graham, M., Milanowski, A. & Miller, J. (2012), Measuring and promoting ınter-rater agreement of teacher and principal performance ratings. Center for Educator Compensation and Reform. ss. 1-33, 26.03.2021 tarihinde eric.ed.gov/?id=ED532068 adresinden edinilmiştir.
31. Hermida, A. (2017). Herkese söyle: Sosyal medyada neden paylaşımda bulunuruz. (A. A., Sabancı, çev.). İstanbul: Epsilon Yayıncılık.
32. Jacob, M. (2020). COVID-19 Accelerates local news trends, for bad and good. 20.05.2021 tarihinde localnewsinitiative.northwestern.edu/posts/2020/04/22/local-news-pandemic/index.html adresinden edinilmiştir.
33. Jurkowitz, M. (2020). Most Americans say COVID-19 has changed news reporting, but many are unsure how it’s affected the industry. 20.05.2021 tarihinde pewresearch.org/fact-tank/2020/05/01/most-americanssay- covid-19-has-changed-news-reporting-but-many-are-unsure-how-its-affected-the-industry adresinden edinilmiştir.
34. Jurkowitz, M. & Mitchell, A. (2020). Americans who primarily get news through social media are least likely to follow COVID-19 coverage, most likely to report seeing made-up news. 20.05.2021 tarihinde journalism. org/2020/03/25/americans-who-primarily-get-news-through-social-media-are-least-likely-to-followcovid- 19-coverage-most-likely-to-report-seeing-made-up-news/ adresinden edinilmiştir.
35. Kayır, O. (2020, April 8). COVID-19 salgını ve yalan haberlerle mücadele. 8.03.2021 tarihinde dijitalmedyavecocuk. bilgi.edu.tr/2020/04/08/covid-19-salgini-ve-yalan-haberlerle-mucadele/ adresinden edinilmiştir.
36. Kim, V. ( 2020, 31 Ocak). No Chinese’: In petitions, signs and tweets, fear is spreading faster than the coronavirus. 18.04.2021 tarihinde latimes.com/world-nation/story/2020-01-31/chinese-tourists-were-a-welcomesource- of-cash-across-asia-now-theyre-a-source-of-panic adresinden edinilmiştir.
37. Kuş, O. (2021). Kovid-19 salgını ve mültecilere yönelik nefret söylemi: Büyük veri perspektifinden metin madenciliği tekniği ile kullanıcı kaynaklı içeriklerin analizi. TRT Akademi, 6(11), 106-131.
38. Li, B. & Scott, O. (2020). Fake news travels fast: Exploring misinformation circulated around Wu Lei’s coronavirus case. International Journal of Sport Communication, 13(3), 505–513.
39. Lovari, A. (2020). Spreading (Dis)trust: Covid-19 misinformation and government intervention in Italy. Media and Communication, 8(2), 458–461.
40. Malhotra, P. (2020). A relationship-centered and culturally ınformed approach to studying misinformation on COVID-19. Social Media and Society, 6(3),1-4.
41. Marconi, F. (2020). A new era of journalism: How Covid-19 is transforming the News. 18.05.2021 tarihinde fpmarconi.medium.com/a-new-era-of-journalism-how-covid-19-is-transforming-the-news- 9f63164f5631 adresinden edinilmiştir.
42. Meza, R., Vincze, H. O. & Mogoş, A. (2018). Targets of online hate speech in context. A comparative digital social science analysis of comments on Public Facebook Pages from Romania and Hungary. Intersections East European Journal of Society and Politics, 4(4),26-50.
43. Montesi, M. (2020). Understanding fake news during the Covid-19 health crisis from the perspective of information behaviour: The case of Spain. Journal of Librarianship and Information Science, 53(3), 1-12.
44. Mozdeh Big Data Text Analysis (2020). Mozdeh Big Data Text Analysis. 01.02. 2021 tarihinde mozdeh.wlv.ac.uk adresinden erişilmiştir.
45. Nagler, R. H., Vogel, R. I., Gollust, S. E., Rothman, A. J., Fowler, E. F. & Yzer, M. C. (2020). Public perceptions of conflicting information surrounding COVID-19: Results from a nationally representative survey of U.S. adults. PLoS ONE, 15(10 October), 1–18.
46. Nguyen, H. & Nguyen, A. (2020). Covid-19 misinformation and the social (Media) amplification of risk: A Vietnamese perspective. Media and Communication, 8(2), 444–447.
47. Paasch-Colberg, S., Strippel, C., Trebbe, J. & Emmer, M. (2021). From insult to hate speech: Mapping offensive language in german user comments on immigration. Media and Communication, 9(1), 171-180.
48. Parekh, B. (2006). Hate speech: Is there a case for banning? Public Policy Research, 12(4), 213-223.
49. Patel, S. S., Moncayo, O. E., Conroy, K. M., Jordan, D. & Erickson, T. B. (2020). The landscape of disinformation on health crisis communication during the COVID-19 pandemic in Ukraine: hybrid warfare tactics, fake media news and review of evidence. Journal of Science Communication, 19(5). 22.03.2021 tarihinde dash.harvard.edu/bitstream/handle/1/37364388/Patel%20-%20JCOM-Ukraine-Disinformation- Review-2020-v2.pdf?sequence=1&isAllowed=y adresinden edinilmiştir.
50. Pınar, Ö. (2020, 31 Ocak). Koronavirüs – İtalya acil durum ilan etti, ülkede Çinlilere saldırılar başladı. 20.02.2021 tarihinde bbc.com/turkce/haberler-dunya-51325331 adresinden edinilmiştir.
51. Radu, R. (2020). Fighting the ‘Infodemic’: Legal responses to COVID-19 Disinformation. Social Media and Society, 6(3),1-4.
52. Ren, J. & Feagin, J. (2021). Face mask symbolism in anti-Asian hate crimes. Ethnic and Racial Studies, 44(5), 1-13.
53. Rodrigues, U. M. & Xu, J. (2020). Regulation of COVID-19 fake news infodemic in China and India. Media International Australia, 177(1), 125–131.
54. Rovetta, A. & Bhagavathula, A. S. (2020). COVID-19-related web search behaviors and infodemic attitudes in Italy: Infodemiological study. JMIR Public Health and Surveillance, 6(2): e19374.
55. Ruiz, N. G., Horowitz, J. M., & Tamir, C. (2020, July 1). Many Black and Asian Americans say they have experienced discrimination amid the COVID‐19 outbreak. Pew Research Center. 18.03.2021 tarihinde pewsocialtrends.org/wp-content/uploads/sites/3/2020/07/PSDT_07.01.20_racism.covid_Full.Report. pdf adresinden edinilmiştir.
56. Sadeghzadeh, M., Abbasi, M., Khajavi, Y. & Amirazodi, H. (2021). Psychological correlates of anxiety in response to COVID-19 outbreak among Iranian University students. Current Psychology, 1-10. https:// doi.org/10.1007/s12144.020.01237-7
57. Soldatova, G., Rasskazova, E., Chigarkova, S., Dementiy, L., Federation, R. & Federation, R. (2020). Click, ıgnore or repost: subjective assessment of the reliability and relevance of information on COVID-19 in the Infodemic. Media Education (Mediaobrazovanie), 60(4), 745–756.
58. Soto-Vásquez, A. D., Gonzalez, A. A., Shi, W., Garcia, N. & Hernandez, J. (2020). COVID-19: Contextualizing misinformation flows in a US Latinx Border Community (Media and communication During COVID-19). Howard Journal of Communications, 1-19. DOI: 10.1080/10646.175.2020.1860839
59. Spivey, M. J. (2017). Fake news and false corroboration: Interactivity in rumor networks. CogSci. 23.03.2021 tarihinde cogsci.mindmodeling.org/2017/papers/0610/paper0610.pdf adresinden edinilmiştir.
60. Starbird, K. (2020). How a crisis researcher makes sense of Covid-19 misinformation. 15.02.2021 tarihinde onezero.medium.com/reflecting-on-the-covid-19-infodemic-as-a-crisis-informatics-researcherce0656fa4d0a adresinden edinilmiştir.
61. Su, Y. (2021). It doesn’t take a village to fall for misinformation: Social media use, discussion heterogeneity preference, worry of the virus, faith in scientists, and COVID-19-related misinformation beliefs. Telematics and Informatics, 58 (December 2020), 101547.
62. Teyit.org (2020). Tablo 2’de bulunan aşı ve virüs ile ilgili konular. 10.03.2021 tarihinde teyit.org adresinden edinilmiştir.
63. Thelwall, M., Kousha, K. & Thelwall, S. (2021). Covid-19 vaccine hesitancy on English-language Twitter. El Profesional de La Información, 30(2), e300212.
64. Tuna Uysal, M. & Tan Eren, G. (2020). COVID-19 salgın sürecinde sosyal medyada yaşlılara yönelik ayrımcılık: Twitter örneği. Turkish Studies, 15(4), 1147-1162.
65. Tunçer, Ç. (2020). Sosyal medya ve şiddet: Ekşi Sözlük’te Çinli algısı. İnsan ve İnsan, 7(25), 65-84.
66. Türk, A. (2020). Koronavirüs (COVID-19) Pandemisi sürecinde yaşlılara yönelik uygulamalar ve yaşlıların psiko-sosyal durumu üzerine bir değerlendirme. Sosyal Hizmet “Social Work”, 2, 35-46.
67. Vosoughi, S., Roy, D. & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146-1151.
68. Vraga, E. K., Tully, M. & Bode, L. (2020). Empowering users to respond to misinformation about Covid-19. Media and Communication, 8(2), 475–479.
69. We Are Social (2020). Digital in Social. 13.05.2021 tarihinde wearesocial.com/digital-2020 adresinden erişilmiştir.
70. We Are Social (2021).Digital in social. 13.05.2021 tarihinde wearesocial.com/digital-2021 adersinden edinilmiştir.
71. Weber, A. (2009). Nefret söylemi el kitabı. (M. Çulhaoğlu, çev.). Strazburg: Avrupa Konseyi Yayınları.
72. Yıldırım, A. (2020). Dijital çağda dijital pandemi: Türkiye’de Covid-19 gündemi. Intermedia International E-journal, 7(13), 381-401.
73. Ziems, C., He, B., Soni, S. & Kumar, S. (2020). Racism is a virus: Anti-Asian hate and counterhate in social media during the COVID-19 crisis. 10.02.2021 tarihinde arXiv preprint arXiv:2005.12423 adresinden edinilmiştir.