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ONLINE PANDEMİ: COVID-19 PANDEMİSİNE İLİŞKİN TWITTER’DA PAYLAŞILAN SAĞLIK BİLGİLERİNİN TEMATİK ANALİZİ

Year 2021, Volume: 9 Issue: 2, 911 - 942, 30.09.2021
https://doi.org/10.19145/e-gifder.916702

Abstract

Twitter, mesajların yazılı veya görüntülü olarak iletebilmesine olanak tanıyan bir sosyal ağ ve bir mikroblog’dur. Genel olarak değerlendirildiğinde Twitter akışı, kullanıcıların görüş ve düşüncelerini ifade edebilecekleri bir ortamken, aynı zamanda güncel olaylara ilişkin kullanıcı tepkilerini ve bakış açılarını içeren bir sosyal medya platformudur. Bu çalışma Twitter’ı, özellikle kriz ve risk dönemlerinde paylaşılan içeriklerin türü, bu içerikleri paylaşan kullanıcıların nitelikleri ile içeriklerin yayılım hızı ve sağlanan etkileşim açısından önemli bir platform olarak değerlendirmekte ve Covid-19 pandemisi sırasında kullanıcıların başvurduğu önemli bir bilgi kaynağı olarak görmektedir. Bu temel görüşten hareket eden çalışmanın amacı, Covid-19 pandemisi sırasında görünürlüğü yüksek olan Türkçe tweet’lerin özelliklerini incelemek, tweet’lerde yer alan ana temaları belirleyerek, bir sosyal medya ortamı olan Twitter'ın, paylaşılan sağlık bilgileri çerçevesinde ürettiği bilgi türlerini ortaya koymaktır. Bu amaçtan hareketle işe koyulan çalışma, tematik içerik analizi tekniğiyle verileri analiz etmekte, analizi de bireylerin sağlık davranışını anlamak için yapılan araştırmalarda kavramsal çerçeve olarak yaygın bir şekilde kullanılan Sağlık İnanç Modeli (SİM) çerçevesinde değerlendirmektedir. Araştırma sonucunda elde edilen bulgulara göre, Twitter’da dolaşıma giren sağlık bilgilerinin SİM’in “eyleme geçirici” olarak tanımladığı ve bireylerin yeni sağlık davranışlarını benimsemelerinde etkili olabilecek bir ortam olarak işlev görebileceği sonucuna ulaşılmıştır.

References

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  • AHMED, Wasim, BARH, Peter, SBAFFI, Laura ve DEMARTINI, Gianluca (2018). “Moral Panic Through the Lens of Twitter: An Analysis of Infectious Disease Outbreaks”, In Proceedings of the 9th International Conference on Social Media and Society, p. 217-221.
  • AHMED, Wasim (2018). Using Twitter Data to Provide Qualitative Insights Into Pandemics and Epidemics, The University of Sheffield Faculty of Social Sciences Information School, Sheffield.
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  • BANDURA, Albert (1977). “Self-Efficacy: Toward a Unifying Theory of Behavioral Change”, Psychological Review, 84(2), p. 191-215.
  • BECKER, Marshall H., DRACHMAN, Robert H. ve KIRSCHT, John P. (1974). “A New Approach to Explaining Sick-Role Behavior in Low-Income Populations”, American Journal of Public Health, 64(3), p. 205-216.
  • BERELSON, Bernard (1952). Content Analysis in Communication Research, Free Press: Glencoe.
  • BERRY, Tanya R., WHARF-HIGGINS, Joan ve NAYLOR, P. J. (2007). “SARS Wars: An Examination of The Quantity and Construction of Health Information in The News Media”, Health Communication, 21(1), p. 35-44.
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  • LUPTON, Deborah (1998). “Medicine and Health Care in Popular Media”, Health Matters: A sociology of Illness, Prevention and Care, p. 194-207.
  • MCCLELLAN, Chandler, ALI, Mir M., MUTTER, Ryan, KROUTIL, Larry ve ANDWEHR, Justin (2017). “Using Social Media to Monitor Mental Health Discussions− evidence From Twitter”, Journal of the American Medical Informatics Association, 24(3), p. 496-502.
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  • MCNEIL, Andrew, HARRIS, Peter R. ve BRIGGS, Pam (2016). “Twitter Influence on UK Vaccination and Antiviral Uptake During the 2009 H1N1 Pandemic”, Frontiers in Public Health, 4, 26.
  • NEIGER, Brad L., THACKERAY, Rosemary, BURTON, Scott H., THACKERAY, Callie R. ve REESE, Jennifer (2013). “Use of Twitter Among Local Health Departments: An Analysis of Information Sharing, Engagement, and Action”, Journal of Medical Internet Research, 15(8).
  • NEUENDORF, Kimberly A. (2002). The Content Analysis Guidebook, Thousand Oaks: Sage Publications.
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  • PRIER, Kyle W., SMITH, Matthew S., GIRAUD-CARRIER, Christophe ve HANSON, Carl L. (2011). “Identifying Health-Related Topics on Twitter. In International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (18-25). Springer: Berlin, Heidelberg.
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ONLINE PANDEMIC: THEMATIC ANALYSIS OF HEALTH INFORMATION SHARED IN TWITTER ABOUT COVID-19 PANDEMIC

Year 2021, Volume: 9 Issue: 2, 911 - 942, 30.09.2021
https://doi.org/10.19145/e-gifder.916702

Abstract

ABSTRACT
Twitter is a social network and a microblog that allows messages to be transmitted in written or visual form. When evaluated in general, Twitter stream is an environment where users can express their opinions and thoughts, it is a social media environment that includes user reactions and perspectives on current events at the same time. This study considers Twitter as an important platform in terms of the type of content shared, especially in times of crisis and risk, the qualities of the users who share these content, the speed of the content and the interaction provided, and the sees it as an important source of information. Based on this basic view, the aim of the study is to examine the characteristics of tweets with high visibility during the Covid-19 pandemic, identify the main themes in Turkish tweets, and to reveal the types of information produced within the framework of health information. Based on this purpose, the study analyzes the data with the thematic content analysis technique, and the analysis is carried out within the framework of the Health Belief Model (HBM), which is widely used as a conceptual framework in researches to understand the health behavior of individuals. According to the findings obtained as a result of the research, it has been concluded that the health information circulating on Twitter can be defined as "cues to action " by SIM and can function as an environment that can be effective for individuals to adopt new health behaviors. 

References

  • ABRAHAM, Charles ve SHEERAN, Paschal (2005). “The Health Belief Model”, Predicting Health Behaviour, 2, p. 28-80.
  • AHMED, Wasim, BARH, Peter, SBAFFI, Laura ve DEMARTINI, Gianluca (2018). “Moral Panic Through the Lens of Twitter: An Analysis of Infectious Disease Outbreaks”, In Proceedings of the 9th International Conference on Social Media and Society, p. 217-221.
  • AHMED, Wasim (2018). Using Twitter Data to Provide Qualitative Insights Into Pandemics and Epidemics, The University of Sheffield Faculty of Social Sciences Information School, Sheffield.
  • BADEMCİ, Vahit (2019). “Geçerlik Nedir? Ne Değildir?”, Eğtim ve Toplum Araştırmaları Dergisi/JRES, 6(2), s. 373-385.
  • BANDURA, Albert (1977). “Self-Efficacy: Toward a Unifying Theory of Behavioral Change”, Psychological Review, 84(2), p. 191-215.
  • BECKER, Marshall H., DRACHMAN, Robert H. ve KIRSCHT, John P. (1974). “A New Approach to Explaining Sick-Role Behavior in Low-Income Populations”, American Journal of Public Health, 64(3), p. 205-216.
  • BERELSON, Bernard (1952). Content Analysis in Communication Research, Free Press: Glencoe.
  • BERRY, Tanya R., WHARF-HIGGINS, Joan ve NAYLOR, P. J. (2007). “SARS Wars: An Examination of The Quantity and Construction of Health Information in The News Media”, Health Communication, 21(1), p. 35-44.
  • BRAUN, Virginia ve CLARKE, Victoria (2006). “Using Thematic Analysis in Psychology”, Qualitative Research in Psychology, 3(2), p. 77-101.
  • BRUNS, Axel ve MOE, Hallvard (2014). “Structural Layers of Communication on Twitter”, Twitter and Society [Digital Formations, Volume 89], p. 15-28.
  • CHAMPION Victoria L. ve SKINNER Celette Sugg (2008). The Health Belief Model. Health Behavior and Health Education: Theory, Research, and Practice. (Editörler) Karen Glanz, Barbara K. Rimer ve K. Viswanath, San Francisco: Jossey-Bass, p. 45-65.
  • CHEW, Cyntia ve EYSENBACH, Gunther (2010). “Pandemics in The Age of Twitter: Content Analysis of Tweets During the 2009 H1N1 Outbreak”, Plos One, 5(11), : e14118. doi:10.1371/journal.pone.0014118
  • DONELLE, Lorie ve BOOTH, Richard G. (2012) "Health Tweets: An Exploration of Health Promotion on Twitter”, The Online Journal of Issues in Nursing, 17(3, 4), Manuscript 4. DOI: 10.3912/OJIN.Vol17No03Man04
  • EYSENBACH, Gunther (2008). “Medicine 2.0: Social Networking, Collaboration, Participation, Apomediation, and Openness”, Journal of Medical Internet Research, 10 (3): e22. doi: 10.2196/jmir.1030.
  • FREELON, Deen ve KARPF, David (2015). “Of Big Birds and Bayonets: Hybrid Twitter Interactivity in The 2012 Presidential Debates”, Information, Communication & Society, 18:4, p. 390-406.
  • GÖZÜM, Sebahat ve ÇAPIK, Cantürk (2014). “Sağlık Davranışlarının Geliştirilmesinde Bir Rehber: Sağlık İnanç Modeli”, Dokuz Eylül Üniversitesi Hemşirelik Fakültesi Elektronik Dergisi, 7(3), s. 230-237. GUIDRY, Jeanine P. D., JIN, Yan, ORR, Caroline A., MESSNER, Marcus ve MEGANCK, Shana (2017). “Ebola on Instagram and Twitter: How Health Organizations Address the Health Crisis in Their Social Media Engagement”, Public Relations Review, 43(3), p. 477-486.
  • GÜR, Nurullah, TATLIYER, Mevlüt ve DİLEK, Şerif (2020). Ekonominin Koronavirüsle Mücadelesi, SETA Yayınları: İstanbul.
  • HACKETT, Alison Jane (2008). “Risk, Its Perception and The Media: The MMR Controversy”. Community Practice, 81(7), p. 22–25.
  • HAN, Jeong Yeob, HAWKİNS, Robert P., SHAW, Bret R., PINGREE, Suzanne, MCTAVISH, Fiona ve GUSTAFSON, David (2009). “Unraveling Uses and Effects of an Interactive Health Communication System”, Journal of Broadcasting & Electronic Media, 53, p. 112–133.
  • HAN, Jeong Yeob (2011). “Transaction Logfile Analysis in Health Communication Research: Challenges and Opportunities”, Patient Education and Counseling, 82, p. 307–312.
  • HIMELBOIM, Itai ve HAN, Jeong Yeob (2014). “Cancer Talk on Twitter: Community Structure and Information Sources in Breast and Prostate Cancer Social Networks”, Journal of Health Communication, 19(2), p. 210-225.
  • HORRIGAN, John ve RAINIE, Lee. (2006). “The Internet’s Growing Role in Life’s Major Moments”,www.pewinternet.org/Reports/2006/The-Internets-Growing-Role-in-Lifes-Major-Moments.aspx, Erişim Tarihi: 01.12.2020.
  • JANSEN, Bernard J., ZHANG, Mimi, SOBEL, Kate ve CHOWDURY, Abdur (2009). “Twitter Power: Tweets as Electronic Word of Mouth”, Journal of the American Society for Information Science and Technology, 60(11), p. 2169-2188.
  • JANZ, Nancy. K. ve BECKER, Marshall. H. (1984). “The Health Belief Model: A Decade Later”, Health Education Quarterly, 11(1), p. 1-47.
  • JONES; Christina L., JENSEN, Jakob D., SCHERR, Courtney L., BROWN, Natasha R., CHRISTY, Kathetyn ve WEAVER, Jeremy (2015). “The Health Belief Model As An Explanatory Framework in Communication Research: Exploring Parallel, Serial, and Moderated Mediation”, Health Communication, 30(6), p. 566-576.
  • KRIPPENDORFF, Klaus (2004). Content Analysis An Introduction to Its Methodology, Thousand Oaks: Sage Publications.
  • LIANG, Hai, FUNG, Isaac Chun-Hai, TSE, Zion Tsz Ho, YIN, Jingjing, CHAN, Chung-Hong, PECHTA, Laura E., ... ve FU, King-Wa (2019). “How Did Ebola Information Spread on Twitter: Broadcasting or Viral Spreading?”, BMC Public Health, 19(1), p. 1-11.
  • LIN, Yu Ru, KEEGAN, Brian, MARGOLIN, Drew ve LAZER, David (2014). “Rising Tides or Rising Stars?: Dynamics of Shared Attention on Twitter During Media Events”, Plos One, 9(5), e94093. doi:10.1371/journal.pone.0094093
  • LIU, Zhiming, LIU, Li ve LI, Hong (2012). “Determinants of Information Retweeting in Microblogging”, Internet Research, 22(4), p. 443-466
  • LOVEJOY, Kristen ve SAXTON, Gregory D. (2012). “Information, Community, and Action: How Nonprofit Organizations Use Social Media, Journal of Computer-Mediated Communication, 17, p. 337–353.
  • LUPTON, Deborah (1998). “Medicine and Health Care in Popular Media”, Health Matters: A sociology of Illness, Prevention and Care, p. 194-207.
  • MCCLELLAN, Chandler, ALI, Mir M., MUTTER, Ryan, KROUTIL, Larry ve ANDWEHR, Justin (2017). “Using Social Media to Monitor Mental Health Discussions− evidence From Twitter”, Journal of the American Medical Informatics Association, 24(3), p. 496-502.
  • MCNAB, Christine (2009). “What Social Media Offers to Health Professionals and Citizens”, Bulletin of the World Health Organization, 87(8), p. 566-566.
  • MCNEIL, Andrew, HARRIS, Peter R. ve BRIGGS, Pam (2016). “Twitter Influence on UK Vaccination and Antiviral Uptake During the 2009 H1N1 Pandemic”, Frontiers in Public Health, 4, 26.
  • NEIGER, Brad L., THACKERAY, Rosemary, BURTON, Scott H., THACKERAY, Callie R. ve REESE, Jennifer (2013). “Use of Twitter Among Local Health Departments: An Analysis of Information Sharing, Engagement, and Action”, Journal of Medical Internet Research, 15(8).
  • NEUENDORF, Kimberly A. (2002). The Content Analysis Guidebook, Thousand Oaks: Sage Publications.
  • PATTON, Michael Quinn (2002). "Designing qualitative studies", Qualitative Research and Evaluation Methods, 3(1), p. 230-246.
  • PRIER, Kyle W., SMITH, Matthew S., GIRAUD-CARRIER, Christophe ve HANSON, Carl L. (2011). “Identifying Health-Related Topics on Twitter. In International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (18-25). Springer: Berlin, Heidelberg.
  • RANTASILA, Anna, SIROLA, Anu, KEKKONEN, Arto, VALASKİVİ, Katja ve KUNELİUS, Risto (2018). “#fukushima Five Years On: A Multimethod Analysis of Twitter on the Anniversary of The Nuclear Disaster”, International Journal of Communication, 12, p. 928-949.
  • ROHLEDER, Poul (2012). Critical Issues in Clinical and Health Psychology, London: Sage Publications.
  • ROSENSTOCK, Irwin M. (1960). “What Research in Motivation Suggests For Public Health”, American Journal of Public Health and the Nations Health, 50(3), p. 295-302.
  • ROSENSTOCK, Irwin M. (1974). “Historical Origins of The Health Belief Model”, Health Education Monographs, 2, p. 328–33.
  • ROSENSTOCK, Irwin M., STRECHER, Victor J. ve BECKER, Marshall H. (1988). “Social Learning Theory and The Health Belief Model”, Health Education Quarterly, 15(2), p. 175-183.
  • RUDAT, Anja, BUDER, Jürgen ve HESSE, Friedrich W. (2014). “Audience Design in Twitter: Retweeting Behavior Between Informational Value and Followers’ Interests”, Computers in Human Behavior, 35, p. 132-139.
  • RUDAT, Anja ve BUDER, Jürgen (2015). “Making Retweeting Social: The Influence of Content and Context Information on Sharing News in Twitter”, Computers in Human Behavior, 46, p. 75-84.
  • SCHMIDT, Katja ve ERNST, Edzard (2004). “Assessing Websites on Complementary and Alternative Medicine for Cancer”, Annals of Oncology, 15(5), p. 733-742.
  • SINGER, Jane B. (2014). “User-Generated Visibility: Secondary Gatekeeping in a Shared Media Space”, New Media & Society, 16(1), p. 55-73.
  • STECKLER, Allan, MCLEROY, Kenneth R., GOODMAN, Robert M., BIRD, Sherly T. ve MCCORMICK, Lauri (1992). “Toward Integrating Qualitative and Quantitative Methods: An Introduction”, Health Education Quarterly, 19(1), p. 1-8.
  • STIEGLITZ, Stefan ve DANG-XUAN, Linh (2013). “Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior”, Journal of Management Information Systems, 29(4), p. 217-248.
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There are 59 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Esra Vona Kurt 0000-0001-8639-9160

Publication Date September 30, 2021
Submission Date April 15, 2021
Published in Issue Year 2021 Volume: 9 Issue: 2

Cite

APA Vona Kurt, E. (2021). ONLINE PANDEMİ: COVID-19 PANDEMİSİNE İLİŞKİN TWITTER’DA PAYLAŞILAN SAĞLIK BİLGİLERİNİN TEMATİK ANALİZİ. Gümüşhane Üniversitesi İletişim Fakültesi Elektronik Dergisi, 9(2), 911-942. https://doi.org/10.19145/e-gifder.916702
AMA Vona Kurt E. ONLINE PANDEMİ: COVID-19 PANDEMİSİNE İLİŞKİN TWITTER’DA PAYLAŞILAN SAĞLIK BİLGİLERİNİN TEMATİK ANALİZİ. e-gifder. September 2021;9(2):911-942. doi:10.19145/e-gifder.916702
Chicago Vona Kurt, Esra. “ONLINE PANDEMİ: COVID-19 PANDEMİSİNE İLİŞKİN TWITTER’DA PAYLAŞILAN SAĞLIK BİLGİLERİNİN TEMATİK ANALİZİ”. Gümüşhane Üniversitesi İletişim Fakültesi Elektronik Dergisi 9, no. 2 (September 2021): 911-42. https://doi.org/10.19145/e-gifder.916702.
EndNote Vona Kurt E (September 1, 2021) ONLINE PANDEMİ: COVID-19 PANDEMİSİNE İLİŞKİN TWITTER’DA PAYLAŞILAN SAĞLIK BİLGİLERİNİN TEMATİK ANALİZİ. Gümüşhane Üniversitesi İletişim Fakültesi Elektronik Dergisi 9 2 911–942.
IEEE E. Vona Kurt, “ONLINE PANDEMİ: COVID-19 PANDEMİSİNE İLİŞKİN TWITTER’DA PAYLAŞILAN SAĞLIK BİLGİLERİNİN TEMATİK ANALİZİ”, e-gifder, vol. 9, no. 2, pp. 911–942, 2021, doi: 10.19145/e-gifder.916702.
ISNAD Vona Kurt, Esra. “ONLINE PANDEMİ: COVID-19 PANDEMİSİNE İLİŞKİN TWITTER’DA PAYLAŞILAN SAĞLIK BİLGİLERİNİN TEMATİK ANALİZİ”. Gümüşhane Üniversitesi İletişim Fakültesi Elektronik Dergisi 9/2 (September 2021), 911-942. https://doi.org/10.19145/e-gifder.916702.
JAMA Vona Kurt E. ONLINE PANDEMİ: COVID-19 PANDEMİSİNE İLİŞKİN TWITTER’DA PAYLAŞILAN SAĞLIK BİLGİLERİNİN TEMATİK ANALİZİ. e-gifder. 2021;9:911–942.
MLA Vona Kurt, Esra. “ONLINE PANDEMİ: COVID-19 PANDEMİSİNE İLİŞKİN TWITTER’DA PAYLAŞILAN SAĞLIK BİLGİLERİNİN TEMATİK ANALİZİ”. Gümüşhane Üniversitesi İletişim Fakültesi Elektronik Dergisi, vol. 9, no. 2, 2021, pp. 911-42, doi:10.19145/e-gifder.916702.
Vancouver Vona Kurt E. ONLINE PANDEMİ: COVID-19 PANDEMİSİNE İLİŞKİN TWITTER’DA PAYLAŞILAN SAĞLIK BİLGİLERİNİN TEMATİK ANALİZİ. e-gifder. 2021;9(2):911-42.