İNFODEMİYOLOJİ, DİJİTAL EPİDEMİYOLOJİ VE METABİLİM: İNSANIN İNSANI, BİLİMİN İNSANI ALDATMASI NASIL ÖNLENİR?

Abstract

Pandemi ve epidemiler güvenilir bilgiye en çok ihtiyaç duyulan dönemler olduğu halde hangi kaynaklara güvenmek gerektiği konusunda kafa karışıklığı oluşmakta, herkes kendileri ve sevdikleri için gerekli önlemleri almak amacıyla her türlü bilgiye, enformasyona kulak kabartmakta, ancak, kendilerine ulaşan bilgilerin hangilerinin doğru hangilerinin yanlış olduğunu değerlendirme olanağından çoğu zaman yoksun bulunmaktadır. Hızla yayılan yanlış bilgilerin etkileri bazı durumlarda hastalığın etkilerinden daha yıkıcı hale gelebilmektedir. Bu nedenle epidemiler sırasında görülen, bazıları doğru bazıları yanlış olan aşırı bilgi bombardımanı anlamına gelen infodemi adı verilen bu durumun iyi yönetilmesi gerekir. Bu durumu inceleme ve neden olduğu sorunlara çözüm bulma çabaları sonucunda infodemiyoloji isimli disiplinler arası bir bilim dalı ortaya çıkmıştır. Pandemi döneminde yaygınlaşan ve infodemi kadar önemli olan bir başka bilgi çağı sorunu bilimsel olmayan yöntemlerle yapılmış araştırma sonuçlarıdır. Yani, infodemi ile yayılan yanlış bilgiler sıradan insanları yanıltmakta iken, bilimsel olmayan yöntemlerle yapılan yayınlar da bilim insanlarını yanıltmaktadır. Her ne kadar bilimsel dergilerde yayınlanmış ve bilim adamı sıfatı taşıyan kişilerce yapılmış, yazılmış olsalar da saygın dergilerde yer alan pek çok araştırmanın aslında bilimsel olmadığı eskiden beri, pandemi öncesinde de bilinen bir gerçektir. Pandemi döneminde herkesin hızla bir şeyler bulup adını duyurma, tarihe geçme arzusu nedeniyle olsa gerek bilimsel araştırma süreçlerinin gerektirdiği sabır ve titizlik geri plana itilmiş, yöntem yönünden son derece zayıf, bulguları tartışmalı olan çok sayıda yayın saygın dergileri istila etmiştir. Bunları inceleme amacını taşıyan Metabilim, diğer adlarıyla Meta-Araştırma ya da Kanıta-Dayalı Araştırma, “bilimin bilimi” ya da “araştırmaların araştırılması” anlamına gelmekte ve her geçen gün önemi artmaktadır.

Keywords

• İnfodemi
• infosürveyans
• dijital epidemiyoloji
• metabilim

Infodemiology, digital epidemiology and metascience: How to manage human-based and science-based misinformation?

Abstract

Although pandemics and epidemics are the periods when reliable information is most needed, there is confusion about which sources to trust. Everyone listens to all kinds of information in order to take the necessary precautions for themselves and their loved ones. However, they usually do not have the opportunity to evaluate which information they receive is true and which is false. The effects of rapidly spreading misinformation can in some cases become more devastating than the effects of the disease. This situation, which is called infodemia and means an excessive information bombardment during pandemics must be managed well. As a result of the efforts to examine this situation and find solutions to the problems, it causes, an interdisciplinary science called infodemiology has emerged. Another information age problem, which has become widespread during the pandemic period is the results of research conducted with non-scientific methods. In other words, while misinformation spread by the infodemic mislead ordinary people, publications made with non-scientific methods also mislead scientists. Although it was published in scientific journals and written by people who have the title of scientist, it is a known fact that many studies in reputable journals are not scientific in fact, even before the pandemic. During the pandemic period, due to the desire of everyone to quickly find something and make a name for themselves, the patience and rigor required by scientific research processes have been pushed into the background, and many publications that are extremely weak in terms of methodology and whose findings are controversial have invaded reputable journals. Metascience, also known as Meta-Research or Evidence-Based Research, means "science of science" or "research of research" and aims to examine such publications.

Keywords

• Infodemia
• infosurveillance
• digital epidemiology
• metascience

References

1. 1. Eysenbach G. Infodemiology: The Epidemiology of (Mis)information. Am J Med. 2002;113:763-5.
2. 2. Eysenbach G. Infodemiology and Infoveillance: Framework for an Emerging Set of Public Health Informatics Methods to Analyze Search, Communication and Publication Behavior on the Internet. J Med Internet Res. 2009;11(1):e11.
3. 3. Rothkopf DJ. When the Buzz Bites Back. The Washington Post. 11 May 2003 [cited 2021 March 19]. Available from: https://www.washingtonpost.com/archive/opinions/2003/05/11/when-the-buzz-bites-back/bc8cd84f-cab6-4648-bf58-0277261af6cd//
4. 4. Coiera E. Information epidemics, economics, and immunity on the internet: We still know so little about the effect of information on public health. BMJ. 1998;317:1469.
5. 5. WHO. WHO public health research agenda for managing infodemics. Geneva: World Health Organization; 2021. Licence: CC BY-NC-SA 3.0 IGO.
6. 6. Syndromic surveillance: systems and analyses. [cited 2021 Jun 3] Available from: https://www.gov.uk/government/collections/syndromic-surveillance-systems-and-analyses#gp-in-hours-syndromic-surveillance-system
7. 7. National Syndromic Surveillance Program (NSSP). [cited 2021 Jun 3] Available from: https://www.cdc.gov/nssp/overview.html
8. 8. Eysenbach G. Infodemiology: tracking flu-related searches on the web for syndromic surveillance. AMIA Annu Symp Proc. 2006:244-8.

9. 9. Salathé M. Digital epidemiology: what is it, and where is it going? Life Sci Soc Policy. 2018;14:1.
10. 10. Grbich C. Qualitative research in health: An introduction. London: Sage; 1999.
11. 11. Kiyimba N, Lester JN, O’Reilly M. Using Naturally Occurring Data in Qualitative Health Research: A Practical Guide. Springer Nature, Switzerland. 2019:31.
12. 12. Domnich A, Panatto D, Signori A, Lai PL, Gasparini R, Amicizia D. Age-related differences in the accuracy of web query based predictions of influenza-like illness. PLoS One. 2015;10:e0127754. Available from: https://doi.org/10.1371/journal.pone.0127754
13. 13. Teng Y, Bi D, Xie G, Jin Y, Huang Y, Lin B, et al. Dynamic forecasting of zika epidemics using google trends. PLoS One. 2017;12:e0165085. Available from: https://doi.org/10.1371/journal.pone.0165085
14. 14. Marques-Toledo CA, Degener CM, Vinhal L, Coelho G, Meira W, Codeço CD, et al. Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level. PLoS Negl Trop Dis. 2017;11:e0005729. Available from: https://doi.org/10.1371/journal.pntd.0005729
15. 15. Lippi G, Cervellin G. Is digital epidemiology reliable?—insight from updated cancer statistics. Ann Transl Med. 2019;7(1):15. doi:10.21037/atm.2018.11.55.
16. 16. Pubmed. [cited 2021 April 28] Available from: https://pubmed.ncbi.nlm.nih.gov/?term=covid-19
17. 17. Schor S, Karten I. Statistical evaluation of medical journal manuscripts. JAMA. 1966;195(13):1123-8. doi : 1 0.1001/jama.1966.03100130097026
18. 18. Pashler H, Wagenmakers E. Editors' introduction to the special section on replicability in psychological science: a crisis of confidence? Perspectives on Psychological Science. 2012;7(6): 528-30. doi: 10.1177/1745691612465253.
19. 19-Ioannidis JPA, Fanelli D, Dunne DD, Goodman SN. Meta-research:Evaluation and Improvement of Research Methods and Practices. PLOS Biology. 2015;13330(10):e1002264. doi: 10.1371/journal.pbio.1002264.
20. 20. Munafò MR, Nosek BA, Bishop DVM, Button KS, Chambers CD, du Sert NP, et al. A manifesto for reproducible science. Nat Hum Behav. 2017;1, 0021. doi: https://doi.org/10.1038/s41562-016-0021.
21. 21. METRICS-Meta-research Innovation Center at Stanford. [cited 2021 May 31] Available from: https://metrics.stanford.edu/research
22. 22. Metascience-The field of research on the scientific process. [cited 2021 May 30] Available from: https://metascience.com
23. 23. Open Science Center. [cited 2021 May 30] Available from: https://www.cos.io
24. 24. Smith GD, Ebrahim S. Data dredging, bias, or confounding. BMJ. 2002;325(7378):1437-8. doi:https://doi.org/10.1136%2Fbmj.325.7378.1437.