Determining the Global Corona Agenda via Google Trends

Öz

During the new coronavirus (COVID-19) pandemic, many fake news and misleading information that could cause fear and panic among the public spread around the world. In this study, we aimed to determine online search behavior and frequency of use of infodemic monikers regarding the COVID-19 outbreak. Searches were made between December 10th, 2019 and October10th, 2020. The top five infodemic and scientific COVID-19 terms trending (coronavirus/koronavirüs, corona/korona, COVID-19, SARS-CoV-2, novel coronavirus/) in Turkish and English in all categories (web, visuals, shopping, etc.) in Turkey and worldwide were searched. It was found that the highest search volume was related to the search term “koronavirus” in Turkey and worldwide. It was determined that the society used infodemic words instead of scientific terms. The word "korona" had highest search volume, while in the more developed regions (Istanbul, İzmir, Bursa, Ankara), "COVID-19”, “SARS-CoV-2” search words were preferred. COVID-19 (value=3), SARS-COV-2 (value <1) and “novel coronavirus (value <1)” search terms were the least preferred search terms worldwide. It was determined that people mostly preferred infodemic monikers. Google Trends (GT) data can use to determine information needs of the public with respect to the disease, public approach and to plan suitable strategies.

Anahtar Kelimeler

• COVID-19
• Google Trends
• Infodemia
• Pandemic

Kaynakça

1. Abd-Alrazaq A, Alhuwail D, Househ M, Hamdi M, Shah Z. 2020. Top concerns of tweeters during the COVID-19 pandemic: infoveillance study. J Med Internet Res, 22(4): e19016.
2. Anonymous, 2020a. URL: https://trends.google.com/trends (access date: Semtember 25, 2020).
3. Anonymous, 2020b. URL: https://unfoundation.org/blog/post/immunizing-the-public-against misinformation/ (access date: Semtember 25, 2020).
4. Badell-Grau RA, Kelly BP, Cuff JP, Lloyd-Evans E, Waller-Evans H. 2020. Investigating the prevalence of reactive online searching in the COVID-19 pandemic: infoveillance study. J Med Internet Res, 22(10): e19791.
5. Bilimsel Danışma Kurulu. 2020. COVID-19 (SARS-CoV-2 Enfeksiyonu) Genel bilgiler, epidemiyoloji ve tanı URL: https://covid19.saglik.gov.tr/Eklenti/39551/0/covid-19rehberigenelbilgilerepidemiyolojivetanipdf.pdf (access date: October 01, 2020).
6. Cancharı CRA. Chávez-Bustamante SG, Caira-Chuquineyra BS. 2020. Exploratory analysis of internet search trends during the COVID-19 outbreak. Revista Cubana de Info en Ciencias de la Salud, 31(3): e1631.
7. Dey M, Zhao SS. 2020. COVID-19 and Kawasaki disease: an analysis using Google Trends. Clinical Rheumat, 39(8): 2483-2484.
8. Eysenbach G. 2011. Infodemiology and infoveillance. Am J Prev Med, 40: 154-158.

9. Higgins TS, Wu AW, Sharma D, Illing EA, Rubel K, Ting JY, Alliance SF. 2020. Correlations of online search engine trends with coronavirus disease (COVID-19) incidence: infodemiology study. JMIR Pub Health Surveil, 6(2): 19702.
10. Husnayain A, Fuad A, Su ECY. 2020. Applications of google search trends for risk communication in infectious disease management: A case study of COVID-19 outbreak in Taiwan. Int J Infect Diseas, 95: 221-223.
11. Jun SP, Yoo HS, Choi S. 2018. Ten years of research change using Google Trends: From the perspective of big data utilizations and applications. Tech Forecast Soc Change, 130: 69-87.
12. Kelvin DJ, Rubino S. 2020. Fear of the novel coronavirus. J Infect Develop Coun, 14(01): 1-2.
13. Khan K, Ramsahai E. 2020. Categorizing 2019-n-cov twitter hashtag data by clustering. SSRN, 11(4): 41-52.
14. Mavragani A, Ochoa G. 2019. Google trends in infodemiology and infoveillance: methodology framework. JMIR Pub Health Surveil, 5(2): 13439.
15. Novel CPERE. 2020. The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China. Zhonghua, 41(2): 145.
16. Nuti SV, Wayda B, Ranasinghe I, Wang S, Dreyer RP, Chen SI. 2014. The use of Google trends in health care research: a systematic review. PLoS One, 9: e109583.
17. Rios RS, Zheng KI, Zheng MH. 2020. Data sharing during COVID-19pandemic: what to take away. Expert Rev Gastroenty Hepatol, 14(12): 1125-1130.
18. Rovetta A, Bhagavathula AS. 2020a. COVID-19-related web search behaviors and infodemic attitudes in italy: Infodemiological study. JMIR Pub Health Surveil, 6(2): e19374.
19. Rovetta A, Bhagavathula AS. 2020b. Global Infodemiology of COVID-19: Focus on Google web searches and Instagram hashtags. MedRxiv, 22(8): e20673.
20. Strzelecki A, Rizun M. 2020. Infodemiological study using google trends on coronavirus epidemic in Wuhan, China. İJOE, 16(4): 139-146.
21. Tausczik Y, Faasse K, Pennebaker JW, Petrie KJ. 2012. Public anxiety and information seeking following the H1N1 outbreak: Blogs, newspaper articles, and Wikipedia visits. Health Commun, 27: 179-185.
22. Uçak NÖ, Al U. 2000. Information Seeking behaviours on the internet. Türk Kütüph,14(3): 317-331.
23. Üçer NA. 2016. Study to examine university students’ use of social media in the context of uses and gratification approach. Global Media J, 6(12): 1-26.
24. Wang C, Horby PW. 2020. Hayden FG, Gao GF. A novel coronavirus outbreak of global health concern. Lancet, 395(10223): 470-473.
25. WHO. 2020. World Health Organization. WHO Coronavirus Disease (COVID-19) Dashboard. URL: https://covid19.who.int/ (access date: October 03, 2020).