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

Year 2022, Volume: 9 Issue: 17, 235 - 254, 31.12.2022
https://doi.org/10.56133/intermedia.1163032

Abstract

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.

References

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

Year 2022, Volume: 9 Issue: 17, 235 - 254, 31.12.2022
https://doi.org/10.56133/intermedia.1163032

Abstract

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.

References

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  • 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
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  • 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
  • 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
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  • Hutto, C., & Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. 8(1).
  • Jo,Taeho. (2019). Text Mining—Concepts, Implementation, and Big Data Challenge (1st ed.). Springer, Cham.
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  • 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
  • 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
  • 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
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  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • McNab, C. (2009). What social media offers to health professionals and citizens. Bulletin of the World Health Organization, 87, 566-566.
  • 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.
  • 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
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  • 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
  • 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
  • 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
  • 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).
  • 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
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There are 62 citations in total.

Details

Primary Language English
Subjects Communication and Media Studies
Journal Section Articles
Authors

Sadettin Demirel 0000-0002-3282-1706

Uğur Gündüz 0000-0002-6138-6758

Publication Date December 31, 2022
Submission Date August 16, 2022
Published in Issue Year 2022 Volume: 9 Issue: 17

Cite

APA Demirel, S., & Gündüz, U. (2022). World Health Organization’s Twitter Use Before and During Covid-19 Pandemic: Sentiment and Textual Analysis of Tweets. Intermedia International E-Journal, 9(17), 235-254. https://doi.org/10.56133/intermedia.1163032

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