Google Trends Analysis of 2023 Türkiye-Syria Earthquake Sequence from a Disaster Management Approach

Yıl 2024, Cilt: 2 Sayı: 2, 23 – 36, 16.07.2024

Zeynep Adak Ahmet Çetinkaya

Öz

6 Şubat 2023 tarihinde, Türkiye ve Suriye’yi sarsan iki yıkıcı deprem meydana geldi ve bölgede felaket boyutunda can kaybına ve zarara neden oldu. Türkiye, aktif bir deprem bölgesidir ve bu coğrafyada etkili bir afet yönetimi için geliştirilecek stratejiler büyük önem taşımaktadır. Bu çalışmada, bölgedeki felaket sonrası dönemde Google arama trendleri analiz edilmektedir. Felaketin ilk haftasındaki “breakout” arama sorguları incelenerek konulara göre kategorize edilmiştir. Arama sorgularında tespit edilen 4 ana konu “Genel deprem bilgisi”, “Önceki felaketlerle karşılaştırma”, “Mevcut durum farkındalığı” ve “Toplumsal toparlanma” olarak belirlenmiştir. Depremden sonraki bir haftayı kapsayan dönem için bu 4 ana konuya olan ilginin seyri incelenmiştir. En fazla hasarın meydana geldiği Hatay ilinde, toplumsal direnç YouTube arama trendleri kullanılarak analiz edilmiştir. YouTube burada dirençli bireysel davranışları ölçmek için bir araç olarak kabul edilmiştir. Felaketi takip eden bir aylık süreçte, Şubat sonunda arama trendlerinde toplum direnci ve iyileşme belirtileri tespit edilmiştir. Son olarak, Afet ve Acil Durum Yönetimi Başkanlığı (AFAD) ile ilgili arama terimlerinin dağılımı incelenmiş ve bu terimlerde gözlemlenen kurumun işlevleri ortaya konulmuştur. Bu işlevler: Toplumu bilgilendirme, tehlike haritalama, yardım faaliyetlerini ve gönüllü arama kurtarma çalışanlarını koordine etme olarak bulunmuştur. Bunlar arasında toplumu bilgilendirme işlevi en yüksek arama hacmine sahiptir. Bu çalışma afete hazırlık, afete karşı direnç ve iyileşme aşamalarında toplumsal, yönetimsel ve bilimsel anlamda faydalı bulgulara sahiptir.

Anahtar Kelimeler

Google Trends, YouTube, Afet Yönetimi, Türkiye-Suriye Depremleri

Kaynakça

• Anadolu-Ajansı. (2023). Kahramanmaraş merkezli depremlerde hayatını kaybedenlerin sayısı 50 bin 96 oldu. Retrieved 19 September from https://www.aa.com.tr/tr/asrin-felaketi/kahramanmaras-merkezli-depremlerde-hayatini-kaybedenlerin-sayisi-50-bin-96-oldu/2850716
• Dal Zilio, L., & Ampuero, J.-P. (2023). Earthquake doublet in Turkey and Syria. Communications Earth & Environment, 4(1). https://doi.org/10.1038/s43247-023-00747-z
• Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012-1014. https://doi.org/10.1038/nature07634
• Hariharan, A., & Park, J. Y. (2021). A flagged or spam? social media driven public interactions for natural disaster response and recovery Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Coimbra, Portugal. https://doi.org/10.1145/3486611.3492224
• Kahramanmaraş ve Hatay Depremleri Raporu. (2023). Retrieved from https://www.sbb.gov.tr/wp-content/uploads/2023/03/2023-Kahramanmaras-ve-Hatay-Depremleri-Raporu.pdf
• Karami, A., Shah, V., Vaezi, R., & Bansal, A. (2020). Twitter speaks: A case of national disaster situational awareness. Journal of Information Science, 46(3), 313-324. https://doi.org/10.1177/0165551519828620
• Linkov, F., Ardalan, A., Hennon, M., Shubnikov, E., Serageldin, I., & Laporte, R. (2010). Using Google Trends to Assess Interest in Disasters. Prehospital and Disaster Medicine, 25(5), 482-484. https://doi.org/10.1017/s1049023x00008608
• Mayer, B. (2019). A Review of the Literature on Community Resilience and Disaster Recovery. Current Environmental Health Reports, 6(3), 167-173. https://doi.org/10.1007/s40572-019-00239-3
• Nuti, S. V., Wayda, B., Ranasinghe, I., Wang, S., Dreyer, R. P., Chen, S. I., & Murugiah, K. (2014). The Use of Google Trends in Health Care Research: A Systematic Review. PLoS ONE, 9(10), e109583. https://doi.org/10.1371/journal.pone.0109583
• Ogie, R. I., James, S., Moore, A., Dilworth, T., Amirghasemi, M., & Whittaker, J. (2022). Social media use in disaster recovery: A systematic literature review. International Journal of Disaster Risk Reduction, 70, 102783. https://doi.org/https://doi.org/10.1016/j.ijdrr.2022.102783
• Parker, D. J. (2020). Disaster resilience – a challenged science. Environmental Hazards, 19(1), 1-9. https://doi.org/10.1080/17477891.2019.1694857
• Pourebrahim, N., Sultana, S., Edwards, J., Gochanour, A., & Mohanty, S. (2019). Understanding communication dynamics on Twitter during natural disasters: A case study of Hurricane Sandy. International Journal of Disaster Risk Reduction, 37, 101176. https://doi.org/https://doi.org/10.1016/j.ijdrr.2019.101176
• Sarker, M. N. I., Peng, Y., Yiran, C., & Shouse, R. C. (2020). Disaster resilience through big data: Way to environmental sustainability. International Journal of Disaster Risk Reduction, 51, 101769. https://doi.org/https://doi.org/10.1016/j.ijdrr.2020.101769
• Sawalha, I. H. (2020). A contemporary perspective on the disaster management cycle. foresight, 22(4), 469-482. https://doi.org/10.1108/fs-11-2019-0097
• Shan, S., Zhao, F., Wei, Y., & Liu, M. (2019). Disaster management 2.0: A real-time disaster damage assessment model based on mobile social media data—A case study of Weibo (Chinese Twitter). Safety Science, 115, 393-413. https://doi.org/https://doi.org/10.1016/j.ssci.2019.02.029
• Tariq, H., Pathirage, C., & Fernando, T. (2021). Measuring community disaster resilience at local levels: An adaptable resilience framework. International Journal of Disaster Risk Reduction, 62, 102358. https://doi.org/https://doi.org/10.1016/j.ijdrr.2021.102358
• Yabe, T., Rao, P. S. C., Ukkusuri, S. V., & Cutter, S. L. (2022). Toward data-driven, dynamical complex systems approaches to disaster resilience. Proceedings of the National Academy of Sciences, 119(8), e2111997119. https://doi.org/10.1073/pnas.2111997119
• Yeo, J., & Knox, C. C. (2019). Public Attention to a Local Disaster Versus Competing Focusing Events: Google Trends Analysis Following the 2016 Louisiana Flood. Social Science Quarterly, 100(7), 2542-2554. https://doi.org/10.1111/ssqu.12666
• Yuan, F., Li, M., Liu, R., Zhai, W., & Qi, B. (2021). Social media for enhanced understanding of disaster resilience during Hurricane Florence. International Journal of Information Management, 57, 102289. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2020.102289

Google Trends Analysis of 2023 Türkiye-Syria Earthquake Sequence from a Disaster Management Approach

Yıl 2024, Cilt: 2 Sayı: 2, 23 – 36, 16.07.2024

Zeynep Adak Ahmet Çetinkaya

Öz

On 6 February 2023, two devastating earthquakes struck Türkiye and Syria, causing catastrophic loss of life and damage in the region. Türkiye is an active earthquake zone, and strategies for an effective disaster management have vital importance in the geography. In this study, Google search trends are analyzed for the period after the disaster in the region. Breakout search queries in the first week of the disaster are analyzed and categorized by topic. “General earthquake knowledge”, “Comparison with previous disasters”, “Situational awareness”, and “Community disaster recovery” were 4 topics identified in queries. Interest trends in the topics are examined for the first week period. Community disaster resilience is assessed using YouTube search trends in Hatay, the province that had the largest damage. YouTube is considered here as a tool to measure resilient individual behaviors. Analyzing a period of one month following the disaster, signs of community resilience and recovery were identified in search trends by the end of February. Lastly, distribution of Disaster and Emergency Management Authority (AFAD)-related search terms are analyzed to figure out the functions of the authority observable in search queries. Informing the community, hazard mapping, coordinating relief activities, and coordinating voluntary rescue and relief workers were the functions identified in breakout search terms. Informing the community had the highest volume in search interests related to AFAD. Results of this study has future implications for disaster management phases of preparedness, resilience and recovery.

Anahtar Kelimeler

Türkiye-Syria Earthquake Sequence, Google Trends, YouTube, Disaster Management

Kaynakça

• Anadolu-Ajansı. (2023). Kahramanmaraş merkezli depremlerde hayatını kaybedenlerin sayısı 50 bin 96 oldu. Retrieved 19 September from https://www.aa.com.tr/tr/asrin-felaketi/kahramanmaras-merkezli-depremlerde-hayatini-kaybedenlerin-sayisi-50-bin-96-oldu/2850716
• Dal Zilio, L., & Ampuero, J.-P. (2023). Earthquake doublet in Turkey and Syria. Communications Earth & Environment, 4(1). https://doi.org/10.1038/s43247-023-00747-z
• Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012-1014. https://doi.org/10.1038/nature07634
• Hariharan, A., & Park, J. Y. (2021). A flagged or spam? social media driven public interactions for natural disaster response and recovery Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Coimbra, Portugal. https://doi.org/10.1145/3486611.3492224
• Kahramanmaraş ve Hatay Depremleri Raporu. (2023). Retrieved from https://www.sbb.gov.tr/wp-content/uploads/2023/03/2023-Kahramanmaras-ve-Hatay-Depremleri-Raporu.pdf
• Karami, A., Shah, V., Vaezi, R., & Bansal, A. (2020). Twitter speaks: A case of national disaster situational awareness. Journal of Information Science, 46(3), 313-324. https://doi.org/10.1177/0165551519828620
• Linkov, F., Ardalan, A., Hennon, M., Shubnikov, E., Serageldin, I., & Laporte, R. (2010). Using Google Trends to Assess Interest in Disasters. Prehospital and Disaster Medicine, 25(5), 482-484. https://doi.org/10.1017/s1049023x00008608
• Mayer, B. (2019). A Review of the Literature on Community Resilience and Disaster Recovery. Current Environmental Health Reports, 6(3), 167-173. https://doi.org/10.1007/s40572-019-00239-3
• Nuti, S. V., Wayda, B., Ranasinghe, I., Wang, S., Dreyer, R. P., Chen, S. I., & Murugiah, K. (2014). The Use of Google Trends in Health Care Research: A Systematic Review. PLoS ONE, 9(10), e109583. https://doi.org/10.1371/journal.pone.0109583
• Ogie, R. I., James, S., Moore, A., Dilworth, T., Amirghasemi, M., & Whittaker, J. (2022). Social media use in disaster recovery: A systematic literature review. International Journal of Disaster Risk Reduction, 70, 102783. https://doi.org/https://doi.org/10.1016/j.ijdrr.2022.102783
• Parker, D. J. (2020). Disaster resilience – a challenged science. Environmental Hazards, 19(1), 1-9. https://doi.org/10.1080/17477891.2019.1694857
• Pourebrahim, N., Sultana, S., Edwards, J., Gochanour, A., & Mohanty, S. (2019). Understanding communication dynamics on Twitter during natural disasters: A case study of Hurricane Sandy. International Journal of Disaster Risk Reduction, 37, 101176. https://doi.org/https://doi.org/10.1016/j.ijdrr.2019.101176
• Sarker, M. N. I., Peng, Y., Yiran, C., & Shouse, R. C. (2020). Disaster resilience through big data: Way to environmental sustainability. International Journal of Disaster Risk Reduction, 51, 101769. https://doi.org/https://doi.org/10.1016/j.ijdrr.2020.101769
• Sawalha, I. H. (2020). A contemporary perspective on the disaster management cycle. foresight, 22(4), 469-482. https://doi.org/10.1108/fs-11-2019-0097
• Shan, S., Zhao, F., Wei, Y., & Liu, M. (2019). Disaster management 2.0: A real-time disaster damage assessment model based on mobile social media data—A case study of Weibo (Chinese Twitter). Safety Science, 115, 393-413. https://doi.org/https://doi.org/10.1016/j.ssci.2019.02.029
• Tariq, H., Pathirage, C., & Fernando, T. (2021). Measuring community disaster resilience at local levels: An adaptable resilience framework. International Journal of Disaster Risk Reduction, 62, 102358. https://doi.org/https://doi.org/10.1016/j.ijdrr.2021.102358
• Yabe, T., Rao, P. S. C., Ukkusuri, S. V., & Cutter, S. L. (2022). Toward data-driven, dynamical complex systems approaches to disaster resilience. Proceedings of the National Academy of Sciences, 119(8), e2111997119. https://doi.org/10.1073/pnas.2111997119
• Yeo, J., & Knox, C. C. (2019). Public Attention to a Local Disaster Versus Competing Focusing Events: Google Trends Analysis Following the 2016 Louisiana Flood. Social Science Quarterly, 100(7), 2542-2554. https://doi.org/10.1111/ssqu.12666
• Yuan, F., Li, M., Liu, R., Zhai, W., & Qi, B. (2021). Social media for enhanced understanding of disaster resilience during Hurricane Florence. International Journal of Information Management, 57, 102289. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2020.102289

Toplam 19 adet kaynakça vardır.

Ayrıntılar

Kaynak Göster

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