COVİD-19 AŞILARININ MARKA ALGILAMALARININ SOSYAL MEDYA ÜZERİNDEN DUYGU ANALİZİ İLE İNCELENMESİ

Öz

İletişim ağları kullanımının giderek hız kazanması markaların sosyal medya kullanıcıları ile iletişimini kolaylaştırmış, kullanıcılar ile markalar düşünce paylaşımı açısından yakınlaşmışlardır. Bununla birlikte birçok fikir ve düşüncenin analiz edilmesi ihtiyacını da gündeme getirmiştir. Kamuoyunda, tüm dünyayı ilgilendiren Covid-19 tedavisine yönelik hangi aşı markasının daha etkili olacağı ve sonucunun ne olacağına ilişkin sağlık endişelerinin yanı sıra artan bir merak ve belirsizlik bulunmaktadır. Bu çalışmada, sosyal medya kullanıcılarının bu aşılara bakış açıları ve bahsi geçen iki markaya karşı çağrışımları henüz yeni bir yöntem olan metin analizi tekniği kullanılarak BioNTech ve Sinovac markaları üzerinden ortaya konmaya çalışılmıştır. Böylece, hem henüz yeni yeni kullanılmaya başlanan söz konusu yöntemin kavranmasıyla ilgili metodolojik bir katkı hem de aşı markalarının imajına etki eden unsurların ne olduğuna dair özetleyici bilgi sunmak hedeflenmiştir. Çalışma kapsamındaki Covid-19 aşılarının incelenmesinde nitel araştırma yöntemlerinden metin madenciliği yapılarak, 15 Mart 2021 ile 15 Nisan 2021 tarihleri arasında, mikro-blog sitesi olarak kullanılan Twitter aracılığı ile BioNTech ve Sinovac aşıları ile ilgili tüm tweetler duygu analizi tekniği ile yorumlanmıştır. Analizde, aşılara ait yorumların pozitif, negatif veya nötr olarak R programlama dili ile sınıflandırılması sağlanmıştır. Elde edilen sonuçlara göre, paylaşılan yorumlarda BioNTech markasına ait tutumların nötr olduğu ve Sinovac markasına göre daha güvenli olarak algılandığı bulunmuştur.

Anahtar Kelimeler

• Marka imajı
• Kovid19 Aşıları
• Sosyal Medya
• Duygu Analizi

ANALYSIS OF BRAND PERCEPTIONS OF COVİD-19 VACCINES WITH SENTIMENT ANALYSIS ON SOCIAL MEDIA

Öz

The increasing use of communication networks have facilitated to communication between brands and social media users, and users and brands have become closer in terms of sharing their thoughts. However, this use has also revived the need to analyze many ideas and thoughts. There is increasing curiosity and uncertainty in the public regarding the treatment of COVID-19 which concerns the entire world, in addition to concerns about which vaccine brand will be more effective and what the outcomes will be. In this study, it was aimed to reveal the perspectives of social media users about these vaccines and through Sinovac and BioNTech brands using text analysis. In the examination of COVID-19 vaccines within the scope of the study, text mining, which is a qualitative research method was performed. All tweets posted on the microblogging site Twitter about the BioNTech and Sinovac vaccines were interpreted using the sentiment analysis technique for the period between 15 March 2021 and 15 April 2021. In the analysis, it was ensured that the interpretations of vaccines were classified with R programming language as positive, negative or neutral. According to the obtained results, the attitudes of users towards the BioNTech brand were neutral, and the brand was perceived to be more trustworthy than the Sinovac brand in the comments shared.

Anahtar Kelimeler

• Brand Image
• Covid 19 Vaccines
• Social Media
• Sentiment Analysis

Kaynakça

1. Referans1 Aker, D.A. (2015). Branding: 20 Key Principles That Drive Success. Istanbul: Mediacat.
2. Referans2 Agrawal, D. Bernstein, p., Bertino, E., Davidson, S. & Dayal, U. (2011). Challenges and Opportunities With Big Data. Available at: https://core.ac.uk/download/pdf/4971223.pdf (accessed 18. 03. 2021 )
3. Referans3 Akın B., & Şimşek, U. T. G. (2018). Creating Value with Social Media Analytics: Forwarding with Sentiment Analysis. Mehmet Akif Ersoy University Journal of the Faculty of Economics and Administrative Sciences, 5(3), 797-811.
4. Referans4 Algul, F. (2014). New Social Movements and Social Media: A Case Study of the Northern Forest Defense Movement. Marmara Communication Journal, 22, 139-152. https://doi.org/ 10.17829/midr.20152214170
5. Referans5 Altunbaş, F. (2014). Social Media and Social Events: Gezi Park Events. https://unt. academia Retrieved from edu/FuatAltunbas. Access Date: 08.03.2021.
6. Referans6 Atay, L. (2020). Covid-19 Outbreak and Its Effects on Tourism. Journal of Travel and Hotel Management 17(1),168-172.
7. Referans7 Ayan, B. (2016). History of Social Networks. Istanbul: Abacus.
8. Referans8 Ayaz, F., (2020). Analysis of Contents for Covid-19 Medicines and Vaccines in Online Newspapers. Electronic Republic Communication Journal, 2(2),48-68.

9. Referans9 Aydin, A.F. (2017). Social Media Usage and Corporate Branding: The Case of Vodafone. Global Media Journal TR Edition, 8(15), 320-345.
10. Referans10 Azap, A. (2020). COVID-19 Vaccines: The Light at the End of the Tunnel. Journal of Archival Sources, 29 (Special Issue 1), 94-100. doi:10.17827/aktd.841264.
11. Referans11 Bloomberght, (2021). Available at: https://www.bloomberght.com/twitter-ile-birlikte-turkiye-de-en-cok-kullanilan-sosyal-medya-sirketlerinin-hepsi-temsilci-atamis-oldu-2277029 (accessed 05.03.2021).
12. Referans12 Foley, S. (2013). When Life Imitates Art: The Arab Spring, the Middle East, and the Modern World, Alternatives: Turkish Journal of International Relations, 12 (3), 32-46.
13. Referans13 Gülcan, T.D. & Bayram, P. (2021). Political Participation in the Covid-19 Process: A Content Analysis on Social Media. International Journal Of Economics, Politics, Humanities & Social Sciences, 4(2),1-11.
14. Referans14 He, W., Zha, S.& Li, L. (2013). Social Media Competitive Analysis And Text Mining: A Case Study İn The Pizza İndustry. International Journal of Information Management, 33(3), 464-472. http://doi.org/10.1016/j.ijinfomgt.2013.01.001
15. Referans15İndyturk,(2020).Availableat:https://www.indyturk.com/node/146321/haber/koronavir%C3%BCs-sosyal-medyaya-nas%C4%B1l-yans%C4%B1d%C4%B1(accessed 14.04.2021)
16. Referans16 İnsites, (2011). Available at: http://blog.insites.eu/2011/09/14/347-millions-europeans-use-social-networks-results-of-aglobal-social-media-study/ (accessed 14.04.2021)
17. Referans17 Işık, N. (2019). Analysis of Social Media Comments for E-Commerce Brands with Text Mining Methods. Istanbul: Master Thesis. Marmara University Institute of Social Sciences.
18. Referans18 Kuyucu M. (2014). Twitter as Environmental News Media and Twitter Journalism. International Refereed Journal of Communication and Literary Studies, 2(3), 159-189.
19. Referans19 Liu, B., (2020). Sentiment Analysis Mining Opinions, Sentiments, and Emotions. United Kingdom: Cambridge University.
20. Referans20 Mohammad & Turney (2013). Crowdsourcing a Word-Emotion Association Lexicon. Available at : http://www.saifmohammad.com/WebDocs/NRCemotionlexicon.pdf (accessed 02.01.2021 )
21. Referans21Oberlo,(2021).Availableat:athttps://www.oberlo.com/blog/twitterstatistics#:~:text=Here's%20a%20summary%20of%20the,female%20and%2066%20percent%20male (accessed 25.04.2021)
22. Referans22 Odabaşı, Y. & Oyman M. (2002). Marketing Communication Management. Eskisehir: Mediacat. Referans23 Oke, C. Y (2018). The Effect of Social Media as an Alternative Media on New Social Movements. Adnan Menderes University fourth force international peer-reviewed journal, 1 (1). 52-61
23. Referans24 Önden, A., Çallı, M.K. & Okan, E.Y. (2018) Brand Crises in Social Media: Case Study with Sentiment Analysis Method. Journal of Management, Marketing and Logistics –JMML, 5(3), 246-254.
24. Referans25 Öztürk, M. F. & Talas, M. (2015). Social Media And Education Interaction. Journal Of World Of Turks (Zfwt), 7(1), 101-120.
25. Referans26 Özüpek M. N. (2004). Social Responsibility in Corporate Image, Institutional and Applied Work. Doctoral Thesis. Konya: Selcuk University, Institute of Social Sciences.
26. Referans27 Sağlık Bakanlığı, (2020). Available at: https://covid19bilgi.saglik.gov.tr/tr/covid-19-yeni-koronavirus-hastaligi-nedir (accessed 28.04.2021)
27. Referans28 Sharda, R., Delen, D., Turban, E., Aronson, J. &Liang, T. (2014). Business intelligence and analytics. System for Decesion Support. Pearson.
28. Referans29 Şenol, Y. & Avcı K. (2019). An Example of Public Institutions' Use of Social Media in the Field of Health: Examination of the Ministry of Health's 2017 Twitter Account. Kocatepe Medical Journal, 20, 52-57.
29. Referans30 Ullah, S., Al-Sehemı. A.G., Klemeš, J.J. Saqıb, S., ,Gondal, S.M.A., Sidra, S., Akasha, A. Hira, S., Ahmad, M., Muhammad, A. Asıf, S. & Bokharı, A. (2021). A Review Of The Progress Of Covıd-19 Vaccine Development. Düzce Journal of the Faculty of Medicine, 23(1),1-23. https//doi.org/ 10.18678/dtfd.890089
30. Referans31 Uraltaş, N. T. & Bahadırlı, L. S. (2012). Electronic Retailing and Social Networking Sites as an Advertising Medium. Social Media Academy. Istanbul: Beta.
31. Referans32 We Are Social. (2020). Digital in 2020 Global Overview Report. Available at: https://wearesocial.com/blog/2021/01/digital-use-around-the-world-in-jan-2021(accessed 22.04.2021)
32. Referans33 Who Coronavirüs, (2021). Available at: https://covid19.who.int/WhoCoronavirus(Covıd-19) Dashboard (accessed 18.03.2021)
33. Referans34 Wohn, D. Y. & Bowe, B. J., (2014). Crystallization: How Social Media Facilitates Social Construction Of Reality, In Proceedings of the Companion Publication of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, 261-264, doi:10.1145/2556420.2556509.