Dünya Sağlık Örgütü’nün Covid-19 Pandemisi Öncesi ve Sırasında Twitter Kullanımı: Tweetlerin Duygu ve Metin Analizi

Öz

Bir kitlesel öz iletişim biçimi olarak sosyal medya dijital yapısı, erişim imkanı ve zaman mekan bağımsızlığı gibi nitelikleri sayesinde hem kullanıcıların hem de organizasyonların, bilgi alma, yayma, enformasyon ve mesajları yeniden dağıtıma sok- ması ve izlemesi için büyük potansiyel ve fırsatlar sunmaktadır. Özellikle kriz durumlarında sosyal medyanın bu gücünü kul- lanabilmek halka doğru bilgi sağlamakla birlikte yanıltıcı iddia ve söylentilerin engellemesinde önemli bir yönetim pratiğidir. Bu varsayımdan hareketle, bu çalışma Dünya Sağlık Örgütü’nün (WHO) Covid-19 pandemisi öncesinde ve pandemi sırasında paylaştığı tweetlerin genel duygu derecelerini ölçmeyi ve karşılaştırmayı amaçlamaktadır. Tweetlerin genel duygu değerleriyle birlikte, tweetlerin içeriği, WHO’nun pandemi öncesinde ve sırasında Twitter kullanımı karşılaştırmalı olarak sağlık iletişimi ve kriz yönetimi çerçevesinde değerlendirilmiştir. Çalışmamız kapsamında R istatistik yazılımı ve ilişkili metin madenciliği kü- tüphaneleri WHO’nun 2018 ve 2021 yılları arasında paylaştığı 34.673 adet tweet üzerinde kullanılarak duygu analizi ve metin analizi uygulanmıştır. Bu çalışma WHO tweetlerini yakın okumaya, duygu ve metin analizine tabi tutarak aynı zamanda kriz yönetim taktiklerini, pandemi öncesi ve sırasında izlenen sağlık politikalarını göstermektedir. Bulgular, WHO’nun pandemi sırasında tweetlerinde kasıtlı olarak olumlu mesajları (sözler ve duygular) tercih ederken, halkı bilgilendirmek ve infodemiyi en aza indirmek için daha fazla tweet paylaştığını ortaya koymaktadır.

Anahtar Kelimeler

• COVID-19
• Sağlık İletişimi
• Kriz İletişimi
• Twitter
• Duygu Analizi
• Metin Analizi

World Health Organization’s Twitter Use Before and During Covid-19 Pandemic: Sentiment and Textual Analysis of Tweets

Öz

Social media as a form of mass self-communication offers a great deal of potential and opportunities for users and organizations to get, diffuse, redistribute, and monitor information and messages due to its features of accessibility, digitality, and time-space independence. Especially during a crisis, being able to harness the power of social media is an im- perative management practice not only to provide the public with accurate information but also to deter misleading claims and rumors regarding the subject. Based on this assumption, this study aims to measure and compare the overall sentiment of the World Health Organization’s (WHO) tweets before and during the coronavirus pandemic. Along with the overall senti- ment of tweets, the contents of tweets, and the WHO’s Twitter use practices before and during the pandemic were assessed in the framework of crisis management and health communication. Within the scope of our study, the WHO’s 34.673 tweets between 2018 and 2021 were examined with sentiment and textual analysis. The study also indicates the health policies be- fore and during the covid19 pandemic and crisis management tactics by analyzing and close reading the WHO’s tweets. The findings reveal that the WHO posted more tweets to inform the public and minimize infodemic while it deliberately preferred positive messages (words and sentiments) in its tweets during the pandemic.

Anahtar Kelimeler

• COVID-19
• Health Communication
• Crisis Communication
• Twitter
• Sentiment Analysis
• Text Analysis.

Kaynakça

1. Åkerlund, M. (2020). The importance of influential users in (re)producing Swedish far-right discourse on Twitter. European Journal of Communication, 35(6), 613–628. https://doi.org/10.1177/0267323120940909
2. Barrie, C., & Ho, J. (2021). academictwitteR: An R package to access the Twitter Academic Research Product Track v2 API endpoint. Journal of Open Source Software, 6(62), 3272. https://doi.org/10.21105/joss.03272
3. Basch, C., Hillyer, G. & Jaime, C. (2022). COVID-19 on TikTok: harnessing an emerging social media platform to convey important public health messages. International Journal of Adolescent Medicine and Health, 34(5), 367- 369. https://doi.org/10.1515/ijamh-2020-0111
4. Benoit, K., Muhr, D., & Watanabe, K. (2021). Multilingual Stopword Lists (2.3) [Computer software]. http://stopwords.quanteda.io/
5. Benoit, K., Watanabe, K., Wang, H., Nulty, P., Obeng, A., Müller, S., & Matsuo, A. (2018). quanteda: An R package for the quantitative analysis of textual data. Journal of Open Source Software, 3(30), 774. https://doi.org/10.21105/joss.00774
6. Broniatowski, D.A, Paul M.J., Dredze, M. (2013). National and Local Influenza Surveillance through Twitter: AnAnalysis of the 2012-2013 Influenza Epidemic. PLoS ONE, 8(12) e83672, 1-8. https://10.1371/journal.pone.0083672
7. Burci, G. L., & Vignes, C. H. (2004). World Health Organization. The Hague: Kluwer Law International.
8. Demirel, S., Kahraman, E., & Gündüz, U. (2022). A text mining analysis of the change in status of the Hagia Sophi- aon Twitter: the political discourse and its reflections on the public opinion. Atlantic Journal of Communication, 1-28. https://doi.org/10.1080/15456870.2022.2093354

9. Denecke, K. and Atique S. (2016). Social Media and Health Crisis Communication During Epidemics. (Eds.)
10. Shabbir S., Gabarron, E., Annie Y.S. L., Participatory Health Through Social Media. Academic Press. 42-66. https://doi.org/10.1016/B978-0-12-809269-9.00004-9
11. Fung, I. C., Tse, Z. T., & Fu, K. W. (2015). The use of social media in public health surveillance. Western Pacific sur- veillance and response journal : WPSAR, 6(2), 3–6. https://doi.org/10.5365/WPSAR.2015.6.1.019
12. Funk, S., Gilad, E., Watkins, C., Jansen, V.A. (2009). The spread of awareness and its impact on epidemic outbreaks. Proceedings of the National Academy of Sciences, 106(16), 6872-6877. https://doi.org/10.1073/pnas.0810762106
13. Gallego, V., Nishiura, H., Sah, R., & Rodriguez-Morales, A. J. (2020). The COVID-19 outbreak and implications for the Tokyo 2020 Summer Olympic Games. Travel Medicine and Infectious Disease, 34, 101604. https://doi. org/10.1016/j.tmaid.2020.101604
14. Gostin, L. O., Sridhar, D., & Hougendobler, D. (2015). The normative authority of the World Health Organization. Public Health, 129(7), 854-863. http://dx.doi.org/10.1016/j.puhe.2015.05.002
15. Grimmer, J., & Stewart, B. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(3), 267-297. doi:10.1093/pan/mps028
16. Guidry, J. P. D., Jin, Y., Orr, C. A., Messner, M., & Meganck, S. (2017). Ebola on Instagram and Twitter: How health organizations address the health crisis in their social media engagement. Public Relations Review, 43(3), 477–486. https://doi.org/10.1016/j.pubrev.2017.04.009
17. Holmes, W. (2020). Crisis communications and social media: advantages, disadvantages and best practices. CCI Symposium. Retrieved from http://trace.tennessee.edu/cgi/viewcontent.cgi?article51003&context5ccisymposium (Accessed 29.04.2020).
18. Hossain, L., Kam, D., Kong, F., Wigand, R. T., & Bossomaier, T. (2016). Social media in Ebola outbreak. Epidemiology & Infection, 144(10), 2136-2143. https://10.1017/S095026881600039X
19. Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.
20. Hutto, C., & Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. 8(1).
21. Jo,Taeho. (2019). Text Mining—Concepts, Implementation, and Big Data Challenge (1st ed.). Springer, Cham.
22. Katz, E. & Lazarsfeld, P.F. (1955). Personal influence: the part played by people in the flow of mass communications. Free Press.
23. Kickbusch, I., & Reddy, K. S. (2015). Global health governance–the next political revolution. public health, 129(7), 838-842. http://dx.doi.org/10.1016/j.puhe.2015.04.014
24. Koiranen, I., Koivula, A., Keipi, T., Saarinen, A. (2019). Shared contexts, shared background, shared values–Homophily in Finnish parliament members’ social networks on Twitter. Telematics and Informatics, 36,117-131. https://doi.org/10.1016/j.tele.2018.11.009
25. Lachlan, K. A., Spence, P. R. & Lin, X. (2014). Expressions of risk awareness and concern through Twitter: On the utility of using the medium as an indication of audience needs. Computers in Human Behavior, 35, 554-559.https://doi.org/10.1016/j.chb.2014.02.029
26. Lee, K. (2009). The World Health Organization (WHO). London: Routledge.
27. Lee, A. (2020). Wuhan novel coronavirus (COVID-19): why global control is challenging? Public health, 179, A1. https://doi.org/10.1016/j.puhe.2020.02.001
28. Lee, A. & Morling, J. (2020). COVID-19: The need for public health in a time of emergency. Public Health, 182, 188. https://doi.org/10.1016/j.puhe.2020.03.027
29. Lewis, S. C., Zamith, R., & Hermida, A. (2013). Content Analysis in an Era of Big Data: A Hybrid Approach to Com- putational and Manual Methods. Journal of Broadcasting & Electronic Media, 57(1), 34–52. https://doi.org/10.1 080/08838151.2012.761702
30. Love, B., Himelboim, I., Holton, A., & Stewart, K. (2013). Twitter as a source of vaccination information: content drivers and what they are saying. American Journal of Infection Control, 41(6), 568-570. http://dx.doi.org/10.1016/j.ajic.2012.10.016
31. McCarthy, M. (2002). A brief history of the World Health Organization. The Lancet, 360(9340), 1111-1112. https:// doi.org/10.1016/S0140-6736(02)11244-X
32. McGregor, S. C. (2019). Social media as public opinion: How journalists use social media to represent public opinion. Journalism, 20(8), 1070-1086. https://doi.org/10.1177/1464884919845458
33. McNab, C. (2009). What social media offers to health professionals and citizens. Bulletin of the World Health Organization, 87, 566-566.
34. Milne, E. M. (2015). Governance for health-grasping at the levers of glocal health. Public health, 129(7), 870-871. http://dx.doi.org/10.1016/j.puhe.2015.06.004.
35. Mohammad, S., & Turney, P. (2013). Crowdsourcing a Word-Emotion Association Lexicon. Computational Intelli- gence, 29(3), 436-465. https://doi.org/10.1111/j.1467-8640.2012.00460.x
36. Naldi, M. (2019). A review of sentiment computation methods with R packages. ArXiv, abs/1901.08319. https://doi.org/10.48550/arXiv.1901.08319
37. OECD. (2015). The changing face of strategic crisis management. Paris: OECD Publishing. Retrieved from: http://dx.doi.org/10.1787/9789264249127-en. (Accessed 29.03.2020)
38. Öztürk, N., Ayvaz, S. (2018). Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis, Telematics and Informatics, 35(1), 136-147. https://doi.org/10.1016/j.tele.2017.10.006
39. Parker, J., Wei, Y., Yates, A., Frieder, O., & Goharian, N. (2013). A framework for detecting public health trends with Twitter. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 556–563. https://doi.org/10.1145/2492517.2492544
40. Pas, de van R., & Schaik, van L. G. (2014). Democratizing the World Health Organization. public health, 128(2),195-201. https://doi.org/10.1016/j.puhe.2013.08.023
41. Pepitone, J. (2010). Twitter users not so social after all. CNNMoney.com. Retrieved from http://money.cnn.com/2010/03/10/technology/twitter_users_active/index.htm?hpt=Mid (Accessed 29.03.2020).
42. Radha, R. (2021). Sağlık diplomasisinin dış politika ve küresel halk sağlığı bağlamında değerlendirilmesi. Journal of Academic Value Studies, 7(2), 127-137. https://doi.org/10.29228/javs.51375
43. R Core Team. (2021). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/
44. Ribeiro, F. N., Araújo, M., Gonçalves, P., André Gonçalves, M., & Benevenuto, F. (2016). SentiBench—A bench- mark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Science, 5(1), 23. https://doi. org/10.1140/epjds/s13688-016-0085-1
45. Rinker, T. (2018). Sentimentr (2.6.1) [R]. https://github.com/trinker/sentimentr (Original work published 2015)
46. Rudnicka, E., Napierała, P., Podfigurna, A., Męczekalski, B., Smolarczyk, R., & Grymowicz, M. (2020). The World- Health Organization (WHO) approach to healthy ageing. Maturitas, 139, 6-11. https://doi.org/10.1016/j.maturi- tas.2020.05.018
47. Silge, J., & Robinson, D. (2017). Text mining with R: A tidy approach. O’Reilly Media, Inc.
48. Sinnenberg, L., Buttenheim, A. M., Padrez, K., Mancheno, C., Ungar, L., & Merchant, R. M. (2017). Twitter as a Tool for Health Research: A Systematic Review. American Journal of Public Health, 107(1), e1–e8. https://doi. org/10.2105/AJPH.2016.303512
49. Sohrabi, C., Alsafi, Z., O'neill, N., Khan, M., Kerwan, A., Al-Jabir, A., ... & Agha, R. (2020). World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). International Journal of Surgery, 76, 71-76. https://doi.org/10.1016/j.ijsu.2020.02.034
50. Sutter, J. D. (2010). Texts, maps battle Haiti cholera outbreak. Retrieved from http://www.cnn.com/2010/TECH/innovation/10/29/haiti.cholera.tech/index.html?hpt=Sbin (Accessed 30.03.2020).
51. Tahamtan, I., Potnis, D., Mohammadi, E., Singh, V., & Miller, L. E. (2022). The Mutual Influence of the World Health Organization (WHO) and Twitter Users During COVID-19: Network Agenda-Setting Analysis. J Med Internet Res, 24(4), e34321. https://doi.org/10.2196/34321
52. Tang, S., Chow, A. Y., Breen, L. J., & Prigerson, H. G. (2020). Can grief be a mental disorder? An online survey on public opinion in mainland China. Death Studies, 44(3), 152-159. https://doi.org/10.1080/07481187.2018.1527415
53. Torales, J., O’Higgins, M., Castaldelli-Maia, J. M., & Ventriglio, A. (2020). The outbreak of COVID-19 coronavirus and its impact on global mental health. International Journal of Social Psychiatry, 66(4), 317–320. https://doi. org/10.1177/0020764020915212
54. Twitter [@Twitter]. (2020, April 7). Over the past few weeks, there have been more than 6 million questions Tweeted about coronavirus/COVID-19. For #WorldHealthDay, we partnered with the @WHO to provide answers to some of your most asked questions. See the answers👇 [Tweet]. Twitter. https://twitter.com/Twitter/status/1247542368514887690
55. Welbers, K., Van Atteveldt, W., & Benoit, K. (2017). Text Analysis in R. Communication Methods and Measures, 11(4), 245–265. https://doi.org/10.1080/19312458.2017.1387238
56. WHO. (1958). The First Ten Years of the World Health Organization. Geneva: WHO Press.
57. Winter, S. & Neubaum, G. (2016). Examining Characteristics of Opinion Leaders in Social Media: A Motivational Approach. Social Media + Society. 2(3), 1–12. https://www.doi.org/10.1177/2056305116665858
58. World Health Organization (WHO) [@WHO]. (2020, March 20). WHO Health Alert brings COVID-19 facts to billions via WhatsApp http://bit.ly/who-covid19-whatsapp https://t.co/uiDbPTHKZa [Tweet]. Twitter. https://twitter.com/WHO/status/1241134713575800834
59. Yoon, S., Elhadad, N., & Bakken, S. (2013). A practical approach for content mining of tweets. American Journal of Preventive Medicine, 45(1), 122-129. https://doi.org/10.1016/j.amepre.2013.02.025
60. Zamith, R., & Lewis, S. C. (2015). Content Analysis and the Algorithmic Coder: What Computational Social Science Means for Traditional Modes of Media Analysis. The ANNALS of the American Academy of Political and Social Science, 659(1), 307–318. https://doi.org/10.1177/0002716215570576
61. Zdunek, R. M. (2022). Qualitative and quantitative social media content analysis: TikTok usage by the World Health Organization during the first wave of the COVID-19 pandemic. In Katarzyna Kopecka-Piech & Bartłomiej Łódzki (Eds.), The Covid-19 Pandemic as a Challenge for Media and Communication Studies. Routledge.
62. Zhang, L. & Fung, A.Y. (2020). Opinion Dynamics Research on Social Media: Breakthroughs and Challenges. Telemathics and Informatics,46. https://doi.org/10.1016/j.tele.2019.101314