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EXAMINATION OF ANTI-VACCINE TWEETS WITH TEXT MINING AND CONTENTS ANALYSIS METHODS

Year 2022, Volume: 25 Issue: 4, 827 - 838, 27.12.2022

Abstract

With the increasing use of social media platforms, public health concerns about the impact of anti-vaccine content on vaccine hesitancy in the community have also increased. During the COVID-19 process, people have mostly shared their thoughts on vaccines via Twitter. This study aims to determine the reasons for vaccine hesitancy in society by examining the tweets about the COVID-19 vaccine by text mining and content analysis methods. In this research, 1258 tweets obtaine with anti-vaccine tags with the ORANGE application were analyzed. A word cloud was created to visualize the most used words in the analyzed tweets. As a result of the research, it has been determined that the most frequently used words are ‘‘plandemi’’ ‘‘biontechsideeffect’’ ‘‘mask’’ and ‘‘covid’’. In the second stage, the analyzed tweets were gathered under 18 themes using the content analysis method. Discourses that do not believe that the pandemic is real constitute 32.8% of tweets. 13% of tweets contain expressions of concern about the side effects and harms of the vaccine. 12.1% of the tweets are statements that think that the vaccine was produced as a biological weapon as a result of the global project. As a result of the study, the emphasis on plandemic, post-vaccine negativities and the themes that the vaccine is a biological weapon came to the fore. It is thought that public information about the side effects of the vaccine and the contents of the vaccines has the potential to reduce vaccine insecurity in society. In addition, it is thought that public health agencies can work together with Twitter and other media outlets to increase positive messages, reduce antagonistic contents, and suspend anti-vaccine accounts such as bots.

References

  • Aaby, P., Kollmann, T. R., & Benn, C.S. (2014). Nonspecific effects of neonatal and infant vaccination: public health, immunological and conceptual challenges. Nature immunology, 15(10), 895-899.
  • Aygün, İ., Kaya, B., & Kaya, M. (2021). Aspect based twitter sentiment analysis on vaccination and vaccine types in covid-19 pandemic with deep learning. IEEE Journal of Biomedical and Health Informatics, 26(5), 2360-2369.
  • Badur, S. (2011). Aşı karşıtı gruplar ve aşılara karşı yapılan haksız suçlamalar. ANKEM Dergisi, 25(2), 82-86.
  • Balli, C., Guzel, M. S., Bostanci, E., & Mishra, A. (2022). Sentimental Analysis of Twitter Users from Turkish Content with Natural Language Processing. Computational Intelligence and Neuroscience, 2022, 1-17.
  • Beşirbellioğlu, B. (2014). Antimikrobiyal aşılar. In P. R. Murray, K. S. Rosenthal & M. A. Pfaller (Eds.), Tıbbi Mikrobiyoloji. (6. Baskı). Atlas Kitapçılık.
  • Betsch, C., Renkewitz, F., Betsch, T., & Ulshöfer, C. (2010). The influence of vaccine-critical websites on perceiving vaccination risks. Journal of health psychology, 15(3), 446-455.
  • Bonnevie, E., Gallegos-Jeffrey, A., Goldbarg, J., Byrd, B., & Smyser, J. (2021). Quantifying the rise of vaccine opposition on Twitter during the COVID-19 pandemic. Journal of communication in healthcare, 14(1), 12-19.
  • Çobaner, A. A., Özüölmez, P. K., & Alkan, N. (2022). Construction of Moral Panic on Twitter About Giving Children Covid-19 Vaccines: The Example of “# DenekDegilBebek”. Erciyes İletişim Dergisi, 9(2), 587-607.
  • Demirhan, K., & Başçoban, A. E. (2021). Covid-19 Salgınında Aşı Karşıtlarının Twitter’da #aşıolmayacağım Etiketi Altında Yaptığı Paylaşımların Kamu Sağlığı Politikaları Bağlamında Analizi. Yeni Medya, 2021(11), 102-115.
  • Drisko, J. W., & Maschi, T. (2016). Content analysis. Pocket Guide to Social Work Re.
  • Durur, F., Akbulut, Y., & Işıkçelik, F. (2022, Temmuz, 30-31). Türkiye’de Aşı Politikaları İçerisinde Aşı Reddinin Yeri. 2. International Mediterranean Scientific Research And Innovation Congress. Girne/KKTC, s.80-93.
  • Elkin, L.S., Topal K., & Bebek G. (2017). Network based model of social media big data predicts contagious disease diffusion. Inf Discov Deliv. 45(3), 110–120.
  • Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of advanced nursing, 62(1), 107-115. Fridman, A., Gershon, R., & Gneezy, A. (2021). COVID-19 and vaccine hesitancy: A longitudinal study. PloS one, 16(4), e0250123.
  • Griffith, J., Marani, H., & Monkman, H. (2021). COVID-19 vaccine hesitancy in Canada: Content analysis of tweets using the theoretical domains framework. Journal of medical internet research, 23(4), e26874.
  • Hou, Z., Tong, Y., Du, F., Lu, L., Zhao, S., Yu, K., Piatek, S. J., Larson, H. J., & Lin, L. (2021). Assessing COVID-19 vaccine hesitancy, confidence, and public engagement: a global social listening study. Journal of medical internet research, 23(6), e27632.
  • Hussain, A., Tahir, A., Hussain, Z., Sheikh, Z., Gogate, M., Dashtipour, K., Ali, A., & Sheikh, A. (2021). Artificial intelligence–enabled analysis of public attitudes on facebook and twitter toward covid-19 vaccines in the united kingdom and the united states: Observational study. Journal of medical internet research, 23(4), e26627.
  • Karafillakis, E., & Larson, H. J. (2017). The benefit of the doubt or doubts over benefits? A systematic literature review of perceived risks of vaccines in European populations. Vaccine, 35(37), 4840-4850.
  • Küçük, D., & Arıcı, N. (2022). Sentiment analysis and stance detection in Turkish tweets about COVID-19 vaccination. (Pantea, K., & Pourya, M. Eds.), In Handbook of Research on Opinion Mining and Text Analytics on Literary Works and Social Media (pp. 371-387). IGI Global.
  • Küçükali, H., Ataç, Ö., Palteki, A. S., Tokaç, A. Z., & Hayran, O. (2022). Vaccine hesitancy and anti-vaccination attitudes during the start of COVID-19 vaccination program: a content analysis on twitter data. Vaccines, 10(2), 161. Krippendorff, K. (1980). Validity in content analysis. In E. Mochmann (Ed.), Computerstrategien für die kommunikationsanalyse (pp. 69-112). Campus.
  • Liew, T. M., & Lee, C. S. (2021). Examining the utility of social media in COVID-19 vaccination: unsupervised learning of 672,133 twitter posts. JMIR public health and surveillance, 7(11), e29789.
  • Lombard, M., Snyder‐Duch, J., & Bracken, C. C. (2002). Content analysis in mass communication: Assessment and reporting of intercoder reliability. Human communication research, 28(4), 587-604.
  • Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Sage.
  • Muric, G., Wu, Y., & Ferrara, E. (2021). COVID-19 vaccine hesitancy on social media: building a public twitter data set of antivaccine content, vaccine misinformation, and conspiracies. JMIR public health and surveillance, 7(11), e30642.
  • Neuendorf, K. A. (2002). The Content Analysis Guidebook. Sage. Neuendorf, K. A. (2017). The Content Analysis Guidebook. (2rd Ed.). Sage.
  • Nuzhath, T., Tasnim, S., Sanjwal, R. K., Trisha, N. F., Rahman, M., Mahmud, S., … Hossain, M. (2020, December 11). COVID-19 vaccination hesitancy, misinformation and conspiracy theories on social media: A content analysis of Twitter data. https://doi.org/10.31235/osf.io/vc9jb
  • Offit, P.A., & Moser, C.A. (2009). The problem with Dr Bob’s alternative vaccine schedule. Pediatrics. 123(1), 164-169.
  • Puri, N., Coomes, E. A., Haghbayan, H., & Gunaratne, K. (2020). Social media and vaccine hesitancy: new updates for the era of COVID-19 and globalized infectious diseases. Human vaccines & immunotherapeutics, 16(11), 2586-2593.
  • Roozenbeek, J., Schneider, C. R., Dryhurst, S., Kerr, J., Freeman, A. L., Recchia, G., Van Der Bles, A. M., & Van Der Linden, S. (2020). Susceptibility to misinformation about COVID-19 around the world. Royal Society open science, 7(10), 201199.
  • Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys (CSUR), 34(1), 1–47. Şirzad, N. (2022). Kovid-19 sürecinde aşı kararsızlığı: aşı karşıtı tweetlere ilişkin bir analiz. TRT Akademi, 7(14), 58-81.
  • Temizhan, E., & Mendeş, M. (2021). COVID-19 pandemisi ile ilgili Twitter mesajlarının metin madenciliği tekniği ile değerlendirilmesi. Türkiye Klinikleri Biyoistatistik, 13(2), 185-200.
  • Thelwall, M., Kousha, K., & Thelwall, S. (2021). Covid-19 vaccine hesitancy on English-language Twitter. Profesional de la información (EPI), 30(2), 1-13.
  • Töreci K. (2012). Aşıların tarihçesi. Aşı Kitabı. 1. Baskı. Akademi Yayıncılık.
  • Troiano, G., & Nardi, A. (2021). Vaccine hesitancy in the era of COVID-19. Public health, 194, 245-251.
  • WHO (2022, Ağustos 25). Ten threats to global health in 2019. World Health Organization. https://www.who.int/news-room/spotlight/ten-threats-to-global-health-in-2019.
  • Yilmaz, G., & Bilen, M. (2022). Sensemaking in a Networked World: COVID-19 Vaccine Hesitancy in Turkey. Communication Studies, 73(4), 347-363.
  • Yousefinaghani, S., Dara, R., Mubareka, S., Papadopoulos, A., & Sharif, S. (2021). An analysis of COVID-19 vaccine sentiments and opinions on Twitter. International Journal of Infectious Diseases, 108, 256-262.

AŞI KARŞITI TWITTER PAYLAŞIMLARININ METİN MADENCİLİĞİ VE İÇERİK ANALİZİ YÖNTEMİYLE İNCELENMESİ

Year 2022, Volume: 25 Issue: 4, 827 - 838, 27.12.2022

Abstract

Sosyal medya platformlarının kullanımının artmasıyla aşı karşıtı içeriklerin toplumdaki aşı reddi üzerindeki etkisine ilişkin halk sağlığı endişeleri de artmıştır. COVID-19 sürecinde insanlar aşılarla ilgili düşüncelerini çoğunlukla Twitter aracılığıyla paylaşmışlardır. Bu çalışmada COVID-19 aşısı ile ilgili atılan tweetleri metin madenciliği ve içerik analizi ile inceleyerek, toplumdaki aşı tereddütünün sebeplerini belirlemek amaçlanmıştır. Bu araştırmada ORANGE uygulaması ile aşı karşıtı etiketlerle atılan 1258 tweet incelenmiştir. İncelenen tweetlerde en çok kullanılan kelimeleri görselleştirmek amacıyla kelime bulutu oluşturulmuştur. Araştırma sonucunda, en sık kullanılan kelimelerin ‘‘plandemi’’ ‘‘biontechyanetki’’ ‘‘maske’’ ve ‘‘covid’’ olduğu tespit edilmiştir. İkinci aşamada, incelenen tweetler içerik analizi yöntemi kullanılarak 18 tema altında toplanmıştır. Pandeminin gerçek olduğuna inanmayan söylemler tweetlerin %32,8’ini oluşturmaktadır. Tweetlerin %13’ü aşının yan etkileri ve zararları konusunda endişe içeren tweetlerdir. Tweetlerin %12,1’i ise aşının küresel proje sonucunda biyolojik silah olarak üretildiğini belirten tweetlerdir. Çalışma sonucunda plandemi vurgusu, aşı sonrası olumsuzluklar ve aşının biyolojik silah olduğu temaları öne çıkmıştır. Aşıların içerikleri, aşı sonrası ortaya çıkabilecek yan etkilerin kamuoyuna aktarılmasının aşıya karşı güvensizliğin aşılmasına yardımcı olabileceği düşünülmektedir. Ayrıca, halk sağlığı kurumlarının, olumlu mesajları artırmak, olumsuz mesajları azaltmak ve botlar gibi aşı karşıtı hesapları askıya almak için Twitter ve diğer medya kuruluşları aracılığıyla çalışabileceği düşünülmektedir.

References

  • Aaby, P., Kollmann, T. R., & Benn, C.S. (2014). Nonspecific effects of neonatal and infant vaccination: public health, immunological and conceptual challenges. Nature immunology, 15(10), 895-899.
  • Aygün, İ., Kaya, B., & Kaya, M. (2021). Aspect based twitter sentiment analysis on vaccination and vaccine types in covid-19 pandemic with deep learning. IEEE Journal of Biomedical and Health Informatics, 26(5), 2360-2369.
  • Badur, S. (2011). Aşı karşıtı gruplar ve aşılara karşı yapılan haksız suçlamalar. ANKEM Dergisi, 25(2), 82-86.
  • Balli, C., Guzel, M. S., Bostanci, E., & Mishra, A. (2022). Sentimental Analysis of Twitter Users from Turkish Content with Natural Language Processing. Computational Intelligence and Neuroscience, 2022, 1-17.
  • Beşirbellioğlu, B. (2014). Antimikrobiyal aşılar. In P. R. Murray, K. S. Rosenthal & M. A. Pfaller (Eds.), Tıbbi Mikrobiyoloji. (6. Baskı). Atlas Kitapçılık.
  • Betsch, C., Renkewitz, F., Betsch, T., & Ulshöfer, C. (2010). The influence of vaccine-critical websites on perceiving vaccination risks. Journal of health psychology, 15(3), 446-455.
  • Bonnevie, E., Gallegos-Jeffrey, A., Goldbarg, J., Byrd, B., & Smyser, J. (2021). Quantifying the rise of vaccine opposition on Twitter during the COVID-19 pandemic. Journal of communication in healthcare, 14(1), 12-19.
  • Çobaner, A. A., Özüölmez, P. K., & Alkan, N. (2022). Construction of Moral Panic on Twitter About Giving Children Covid-19 Vaccines: The Example of “# DenekDegilBebek”. Erciyes İletişim Dergisi, 9(2), 587-607.
  • Demirhan, K., & Başçoban, A. E. (2021). Covid-19 Salgınında Aşı Karşıtlarının Twitter’da #aşıolmayacağım Etiketi Altında Yaptığı Paylaşımların Kamu Sağlığı Politikaları Bağlamında Analizi. Yeni Medya, 2021(11), 102-115.
  • Drisko, J. W., & Maschi, T. (2016). Content analysis. Pocket Guide to Social Work Re.
  • Durur, F., Akbulut, Y., & Işıkçelik, F. (2022, Temmuz, 30-31). Türkiye’de Aşı Politikaları İçerisinde Aşı Reddinin Yeri. 2. International Mediterranean Scientific Research And Innovation Congress. Girne/KKTC, s.80-93.
  • Elkin, L.S., Topal K., & Bebek G. (2017). Network based model of social media big data predicts contagious disease diffusion. Inf Discov Deliv. 45(3), 110–120.
  • Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of advanced nursing, 62(1), 107-115. Fridman, A., Gershon, R., & Gneezy, A. (2021). COVID-19 and vaccine hesitancy: A longitudinal study. PloS one, 16(4), e0250123.
  • Griffith, J., Marani, H., & Monkman, H. (2021). COVID-19 vaccine hesitancy in Canada: Content analysis of tweets using the theoretical domains framework. Journal of medical internet research, 23(4), e26874.
  • Hou, Z., Tong, Y., Du, F., Lu, L., Zhao, S., Yu, K., Piatek, S. J., Larson, H. J., & Lin, L. (2021). Assessing COVID-19 vaccine hesitancy, confidence, and public engagement: a global social listening study. Journal of medical internet research, 23(6), e27632.
  • Hussain, A., Tahir, A., Hussain, Z., Sheikh, Z., Gogate, M., Dashtipour, K., Ali, A., & Sheikh, A. (2021). Artificial intelligence–enabled analysis of public attitudes on facebook and twitter toward covid-19 vaccines in the united kingdom and the united states: Observational study. Journal of medical internet research, 23(4), e26627.
  • Karafillakis, E., & Larson, H. J. (2017). The benefit of the doubt or doubts over benefits? A systematic literature review of perceived risks of vaccines in European populations. Vaccine, 35(37), 4840-4850.
  • Küçük, D., & Arıcı, N. (2022). Sentiment analysis and stance detection in Turkish tweets about COVID-19 vaccination. (Pantea, K., & Pourya, M. Eds.), In Handbook of Research on Opinion Mining and Text Analytics on Literary Works and Social Media (pp. 371-387). IGI Global.
  • Küçükali, H., Ataç, Ö., Palteki, A. S., Tokaç, A. Z., & Hayran, O. (2022). Vaccine hesitancy and anti-vaccination attitudes during the start of COVID-19 vaccination program: a content analysis on twitter data. Vaccines, 10(2), 161. Krippendorff, K. (1980). Validity in content analysis. In E. Mochmann (Ed.), Computerstrategien für die kommunikationsanalyse (pp. 69-112). Campus.
  • Liew, T. M., & Lee, C. S. (2021). Examining the utility of social media in COVID-19 vaccination: unsupervised learning of 672,133 twitter posts. JMIR public health and surveillance, 7(11), e29789.
  • Lombard, M., Snyder‐Duch, J., & Bracken, C. C. (2002). Content analysis in mass communication: Assessment and reporting of intercoder reliability. Human communication research, 28(4), 587-604.
  • Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Sage.
  • Muric, G., Wu, Y., & Ferrara, E. (2021). COVID-19 vaccine hesitancy on social media: building a public twitter data set of antivaccine content, vaccine misinformation, and conspiracies. JMIR public health and surveillance, 7(11), e30642.
  • Neuendorf, K. A. (2002). The Content Analysis Guidebook. Sage. Neuendorf, K. A. (2017). The Content Analysis Guidebook. (2rd Ed.). Sage.
  • Nuzhath, T., Tasnim, S., Sanjwal, R. K., Trisha, N. F., Rahman, M., Mahmud, S., … Hossain, M. (2020, December 11). COVID-19 vaccination hesitancy, misinformation and conspiracy theories on social media: A content analysis of Twitter data. https://doi.org/10.31235/osf.io/vc9jb
  • Offit, P.A., & Moser, C.A. (2009). The problem with Dr Bob’s alternative vaccine schedule. Pediatrics. 123(1), 164-169.
  • Puri, N., Coomes, E. A., Haghbayan, H., & Gunaratne, K. (2020). Social media and vaccine hesitancy: new updates for the era of COVID-19 and globalized infectious diseases. Human vaccines & immunotherapeutics, 16(11), 2586-2593.
  • Roozenbeek, J., Schneider, C. R., Dryhurst, S., Kerr, J., Freeman, A. L., Recchia, G., Van Der Bles, A. M., & Van Der Linden, S. (2020). Susceptibility to misinformation about COVID-19 around the world. Royal Society open science, 7(10), 201199.
  • Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys (CSUR), 34(1), 1–47. Şirzad, N. (2022). Kovid-19 sürecinde aşı kararsızlığı: aşı karşıtı tweetlere ilişkin bir analiz. TRT Akademi, 7(14), 58-81.
  • Temizhan, E., & Mendeş, M. (2021). COVID-19 pandemisi ile ilgili Twitter mesajlarının metin madenciliği tekniği ile değerlendirilmesi. Türkiye Klinikleri Biyoistatistik, 13(2), 185-200.
  • Thelwall, M., Kousha, K., & Thelwall, S. (2021). Covid-19 vaccine hesitancy on English-language Twitter. Profesional de la información (EPI), 30(2), 1-13.
  • Töreci K. (2012). Aşıların tarihçesi. Aşı Kitabı. 1. Baskı. Akademi Yayıncılık.
  • Troiano, G., & Nardi, A. (2021). Vaccine hesitancy in the era of COVID-19. Public health, 194, 245-251.
  • WHO (2022, Ağustos 25). Ten threats to global health in 2019. World Health Organization. https://www.who.int/news-room/spotlight/ten-threats-to-global-health-in-2019.
  • Yilmaz, G., & Bilen, M. (2022). Sensemaking in a Networked World: COVID-19 Vaccine Hesitancy in Turkey. Communication Studies, 73(4), 347-363.
  • Yousefinaghani, S., Dara, R., Mubareka, S., Papadopoulos, A., & Sharif, S. (2021). An analysis of COVID-19 vaccine sentiments and opinions on Twitter. International Journal of Infectious Diseases, 108, 256-262.
There are 36 citations in total.

Details

Primary Language Turkish
Subjects Health Care Administration
Journal Section Makaleler
Authors

Kübra Sütcü 0000-0003-2346-9867

Burak Tekerek 0000-0001-7617-2368

Gökçen Özler 0000-0003-3099-5654

Publication Date December 27, 2022
Published in Issue Year 2022 Volume: 25 Issue: 4

Cite

APA Sütcü, K., Tekerek, B., & Özler, G. (2022). AŞI KARŞITI TWITTER PAYLAŞIMLARININ METİN MADENCİLİĞİ VE İÇERİK ANALİZİ YÖNTEMİYLE İNCELENMESİ. Hacettepe Sağlık İdaresi Dergisi, 25(4), 827-838.
AMA Sütcü K, Tekerek B, Özler G. AŞI KARŞITI TWITTER PAYLAŞIMLARININ METİN MADENCİLİĞİ VE İÇERİK ANALİZİ YÖNTEMİYLE İNCELENMESİ. HSİD. December 2022;25(4):827-838.
Chicago Sütcü, Kübra, Burak Tekerek, and Gökçen Özler. “AŞI KARŞITI TWITTER PAYLAŞIMLARININ METİN MADENCİLİĞİ VE İÇERİK ANALİZİ YÖNTEMİYLE İNCELENMESİ”. Hacettepe Sağlık İdaresi Dergisi 25, no. 4 (December 2022): 827-38.
EndNote Sütcü K, Tekerek B, Özler G (December 1, 2022) AŞI KARŞITI TWITTER PAYLAŞIMLARININ METİN MADENCİLİĞİ VE İÇERİK ANALİZİ YÖNTEMİYLE İNCELENMESİ. Hacettepe Sağlık İdaresi Dergisi 25 4 827–838.
IEEE K. Sütcü, B. Tekerek, and G. Özler, “AŞI KARŞITI TWITTER PAYLAŞIMLARININ METİN MADENCİLİĞİ VE İÇERİK ANALİZİ YÖNTEMİYLE İNCELENMESİ”, HSİD, vol. 25, no. 4, pp. 827–838, 2022.
ISNAD Sütcü, Kübra et al. “AŞI KARŞITI TWITTER PAYLAŞIMLARININ METİN MADENCİLİĞİ VE İÇERİK ANALİZİ YÖNTEMİYLE İNCELENMESİ”. Hacettepe Sağlık İdaresi Dergisi 25/4 (December 2022), 827-838.
JAMA Sütcü K, Tekerek B, Özler G. AŞI KARŞITI TWITTER PAYLAŞIMLARININ METİN MADENCİLİĞİ VE İÇERİK ANALİZİ YÖNTEMİYLE İNCELENMESİ. HSİD. 2022;25:827–838.
MLA Sütcü, Kübra et al. “AŞI KARŞITI TWITTER PAYLAŞIMLARININ METİN MADENCİLİĞİ VE İÇERİK ANALİZİ YÖNTEMİYLE İNCELENMESİ”. Hacettepe Sağlık İdaresi Dergisi, vol. 25, no. 4, 2022, pp. 827-38.
Vancouver Sütcü K, Tekerek B, Özler G. AŞI KARŞITI TWITTER PAYLAŞIMLARININ METİN MADENCİLİĞİ VE İÇERİK ANALİZİ YÖNTEMİYLE İNCELENMESİ. HSİD. 2022;25(4):827-38.