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COVID-19 PANDEMİSİ VE ÖNCESİNDE HALKIN FARKLI DUYGU DURUM İFADELERİNE İLGİSİNİN GOOGLE TRENDLER ÜZERİNDEN ANALİZİ

Year 2023, Volume: 6 Issue: 3, 267 - 282, 31.07.2023

Derya Gündüz Hoşgör Hacer Güngördü Haydar Hoşgör

https://doi.org/10.33723/rs.1303402

Abstract

Bu çalışmanın temel amacı Covid-19 pandemisi sırasında ve öncesinde halkın farklı duygu durum ifadelerine yönelik ilgisinde istatistiki olarak anlamlı bir farklılık olup olmadığının incelenmesidir. Çalışmanın verileri ücretsiz ve halka açık bir veri tabanı olan Google Trendler’den elde edilmiştir. Covid-19 öncesi dönemi için Mart 2019-2020 yıl aralığı, pandemi dönemi içinse Mart 2020-2021 dönemi referans alınmış ve 25 adet arama terimi taranmıştır. Tarama işlemi yapılırken Türkiye ve tüm kategoriler seçilerek aramalar yoğunlaştırılmıştır. 20-23 Şubat 2023 tarihleri arasında toplanan verilerin analizinde Student’s t-testi kullanılmıştır. Pandemi öncesi dönemde en fazla arama hacmi ortalamasına sahip olan ilk üç terimin sırayla “ölüm” (81,6), “anksiyete” (79,0), “depresyon” (74,4); pandemi dönemindekilerin ise sırayla “halüsinasyon” (66,9), “anksiyete” (64,9) ve “öfke” (54,9) olduğu saptanmıştır. “Melankoli”, “belirsizlik”, “paranoya” ve “halüsinasyon” terimlerinin Google’da aranma sıklıklarının Covid-19 pandemisi döneminde istatistiki olarak anlamlı derecede (p<0,05) arttığı belirlenmiştir. “Anksiyete”, “depresyon”, “stres”, “panik atak”, “kaygı”, “intihar”, “korku”, “ölüm”, “yalnızlık”, “takıntı”, “çaresizlik” ve “can sıkıntısı” terimlerinin ortalama aranma sıklıklarının ise şaşırtıcı olarak Covid-19 pandemisi öncesinde istatistiki olarak anlamlı derecede arttığı (p<0,05) tespit edilmiştir. Bu sonuçlar, pandeminin halkın duygusal iyi oluş hali üzerinde derin etkileri olduğunu ve ruhsal sağlığa etkilerine yönelik müdahalelere ihtiyaç duyulduğunu göstermektedir.

Keywords

Covid-19 Pandemi Google Trendler Duygu Durumu Türkiye

Supporting Institution

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Project Number

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Thanks

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References

  • Adams-Prassl, A., Boneva, T., Golin, M., & Rauh, C. (2022). The impact of the coronavirus lockdown on mental health: evidence from the United States. Economic Policy, 37(109), 139-155. https://doi.org/10.1093/epolic/eiac002
  • Avcı, K. (2021). Türkiye’de COVID-19 ile ilgili web arama davranışlarının incelenmesi: Google trendleri kullanan bir dijital epidemiyoloji çalışması. Türk Hijyen ve Deneysel Biyoloji Dergisi, 78(2), 133-146. https://dx.doi.org/10.5505/TurkHijyen.2021.48991
  • Brodeur, A., Clark, A. E., Fleche, S., & Powdthavee, N. (2021). COVID-19, lockdowns and well-being: Evidence from Google Trends. Journal of Public Economics, 193, 104346. https://doi.org/10.1016/j.jpubeco.2020.104346
  • Chintalapudi, N., Battineni, G., & Amenta, F. (2021). Sentimental analysis of COVID-19 tweets using deep learning models. Infectious Disease Reports, 13(2), 329-339. https://doi.org/10.3390/idr13020032
  • Dubey, S., Biswas, P., Ghosh, R., Chatterjee, S., Dubey, M. J., Chatterjee, S., ... & Lavie, C. J. (2020). Psychosocial impact of COVID-19. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(5), 779-788. https://doi.org/10.1016/j.dsx.2020.05.035
  • Gao, J., Zheng, P., Jia, Y., Chen, H., Mao, Y., Chen, S., ... & Dai, J. (2020). Mental health problems and social media exposure during COVID-19 outbreak. Plos One, 15(4), e0231924. https://doi.org/10.1371/journal.pone.0231924
  • Gianfredi, V., Provenzano, S., & Santangelo, O. E. (2021). What can internet users' behaviours reveal about the mental health impacts of the COVID-19 pandemic? A systematic review. Public Health, 198, 44-52. https://doi.org/10.1016/j.puhe.2021.06.024
  • Hagen, D., Lai, A. Y., & Goldmann, E. (2022). Trends in negative emotions throughout the COVID-19 pandemic in the United States. Public Health, 212, 4-6. https://doi.org/10.1016/j.puhe.2022.08.009
  • Hoerger, M., Alonzi, S., Perry, L. M., Voss, H. M., Easwar, S., & Gerhart, J. I. (2020). Impact of the COVID-19 pandemic on mental health: Real-time surveillance using Google Trends. Psychological Trauma: Theory, Research, Practice, and Policy, 12(6), 567-568. https://psycnet.apa.org/doi/10.1037/tra0000872
  • Kristoufek, L., Moat, H. S., & Preis, T. (2016). Estimating suicide occurrence statistics using Google Trends. EPJ data science, 5, 1-12. http://dx.doi.org/10.1140/epjds/s13688-016-0094-0
  • Li, X., Zhou, M., Wu, J., Yuan, A., Wu, F., & Li, J. (2020). Analyzing COVID-19 on online social media: Trends, sentiments and emotions. ArXiv Preprint ArXiv:2005.14464. https://doi.org/10.48550/arXiv.2005.14464
  • Lwin, M. O., Lu, J., Sheldenkar, A., Schulz, P. J., Shin, W., Gupta, R., & Yang, Y. (2020). Global sentiments surrounding the COVID-19 pandemic on Twitter: analysis of Twitter trends. JMIR Public Health and Surveillance, 6(2), e19447. https://doi.org/10.2196/19447
  • Niederkrotenthaler, T., Fu, K. W., Yip, P. S., Fong, D. Y., Stack, S., Cheng, Q., & Pirkis, J. (2012). Changes in suicide rates following media reports on celebrity suicide: A meta-analysis. Journal of Epidemiol Community Health, 66(11), 1037-1042. http://dx.doi.org/10.1136/jech-2011-200707
  • Pfefferbaum, B., & North, C. S. (2020). Mental health and the Covid-19 pandemic. New England Journal of Medicine, 383(6), 510-512. https://doi.org/10.1056/NEJMp2008017
  • Rogers, J. P., Chesney, E., Oliver, D., Pollak, T. A., McGuire, P., Fusar-Poli, P., ... & David, A. S. (2020). Psychiatric and neuropsychiatric presentations associated with severe coronavirus infections: A systematic review and meta-analysis with comparison to the COVID-19 pandemic. The Lancet Psychiatry, 7(7), 611-627. https://doi.org/10.1016/S2215-0366(20)30203-0
  • Southwick, L., Guntuku, S. C., Klinger, E. V., Seltzer, E., McCalpin, H. J., & Merchant, R. M. (2021). Characterizing COVID-19 content posted to TikTok: public sentiment and response during the first phase of the COVID-19 pandemic. Journal of Adolescent Health, 69(2), 234-241. https://doi.org/10.1016/j.jadohealth.2021.05.010
  • Taquet, M., Geddes, J. R., Husain, M., Luciano, S., & Harrison, P. J. (2021). 6-month neurological and psychiatric outcomes in 236 379 survivors of COVID-19: A retrospective cohort study using electronic health records. The Lancet Psychiatry, 8(5), 416-427. https://doi.org/10.1371/journal.pmed.1003773
  • Ü. Dörttepe, Z., Hoşgör, H., & Sağcan, H. (2021). The effect of COVID-19 phobia on perceived stress: The sample of prehospital emergency care professionals. Journal of Academic Value Studies, 7(1), 31-40. https://doi.org/10.29228/javs.49250
  • Wang, C., Pan, R., Wan, X., Tan, Y., Xu, L., Ho, C. S., & Ho, R. C. (2020). Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China. International Journal of Environmental Research and Public Health, 17(5), 1729. https://doi.org/10.3390/ijerph17051729
  • Wang, J., Fan, Y., Palacios, J., Chai, Y., Guetta-Jeanrenaud, N., Obradovich, N., ... & Zheng, S. (2022). Global evidence of expressed sentiment alterations during the COVID-19 pandemic. Nature Human Behaviour, 6(3), 349-358. https://doi.org/10.1038/s41562-022-01312-y
  • Xiong, J., Lipsitz, O., Nasri, F., Lui, L. M., Gill, H., Phan, L., ... & McIntyre, R. S. (2020). Impact of COVID-19 pandemic on mental health in the general population: A systematic review. Journal of Affective Disorders, 277, 55-64. https://doi.org/10.1016/j.jad.2020.08.001

Analysis of Public Interest in Different Emotional State Expressions During and Before the COVID-19 Pandemic Using Google Trends

Year 2023, Volume: 6 Issue: 3, 267 - 282, 31.07.2023

Derya Gündüz Hoşgör Hacer Güngördü Haydar Hoşgör

https://doi.org/10.33723/rs.1303402

Abstract

The main objective of this study is to examine whether there is a statistically significant difference in the public's interest in different emotional expressions during and before the Covid-19 pandemic. Data for the study was obtained from Google Trends, a publicly available database. The pre-pandemic period was set from March 2019-2020, and the pandemic period was set from March 2020-2021. 25 search terms related to emotional expressions were scanned, and searches were focused in Turkey across all categories. Student's t-test was used for data analysis collected between February 20-23, 2023. The top three search terms with the highest average search volume during the pre-pandemic period were "death" (81.6), "anxiety" (79.0), and "depression" (74.4), respectively. During the pandemic period, the top three were "hallucination" (66.9), "anxiety" (64.9), and "anger" (54.9), respectively. It was found that the search frequencies of the terms "melancholy", "uncertainty", "paranoia", and "hallucination" on Google increased significantly (p<0.05) during the Covid-19 pandemic period. Surprisingly, it was also found that the average search frequencies of the terms "anxiety", "depression", "stress", "panic attack", "anxiousness", "suicide", "fear", "death", "loneliness", "obsession", "despair", and "boredom" also significantly increased (p<0.05) before the Covid-19 pandemic. These findings highlight the need for interventions to address the mental health consequences of the pandemic, and demonstrate the usefulness of Google Trends as a tool for monitoring public interest in emotional expressions.

Keywords

Covid-19 Pandemic Google Trends Emotional State Turkey

Project Number

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References

  • Adams-Prassl, A., Boneva, T., Golin, M., & Rauh, C. (2022). The impact of the coronavirus lockdown on mental health: evidence from the United States. Economic Policy, 37(109), 139-155. https://doi.org/10.1093/epolic/eiac002
  • Avcı, K. (2021). Türkiye’de COVID-19 ile ilgili web arama davranışlarının incelenmesi: Google trendleri kullanan bir dijital epidemiyoloji çalışması. Türk Hijyen ve Deneysel Biyoloji Dergisi, 78(2), 133-146. https://dx.doi.org/10.5505/TurkHijyen.2021.48991
  • Brodeur, A., Clark, A. E., Fleche, S., & Powdthavee, N. (2021). COVID-19, lockdowns and well-being: Evidence from Google Trends. Journal of Public Economics, 193, 104346. https://doi.org/10.1016/j.jpubeco.2020.104346
  • Chintalapudi, N., Battineni, G., & Amenta, F. (2021). Sentimental analysis of COVID-19 tweets using deep learning models. Infectious Disease Reports, 13(2), 329-339. https://doi.org/10.3390/idr13020032
  • Dubey, S., Biswas, P., Ghosh, R., Chatterjee, S., Dubey, M. J., Chatterjee, S., ... & Lavie, C. J. (2020). Psychosocial impact of COVID-19. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(5), 779-788. https://doi.org/10.1016/j.dsx.2020.05.035
  • Gao, J., Zheng, P., Jia, Y., Chen, H., Mao, Y., Chen, S., ... & Dai, J. (2020). Mental health problems and social media exposure during COVID-19 outbreak. Plos One, 15(4), e0231924. https://doi.org/10.1371/journal.pone.0231924
  • Gianfredi, V., Provenzano, S., & Santangelo, O. E. (2021). What can internet users' behaviours reveal about the mental health impacts of the COVID-19 pandemic? A systematic review. Public Health, 198, 44-52. https://doi.org/10.1016/j.puhe.2021.06.024
  • Hagen, D., Lai, A. Y., & Goldmann, E. (2022). Trends in negative emotions throughout the COVID-19 pandemic in the United States. Public Health, 212, 4-6. https://doi.org/10.1016/j.puhe.2022.08.009
  • Hoerger, M., Alonzi, S., Perry, L. M., Voss, H. M., Easwar, S., & Gerhart, J. I. (2020). Impact of the COVID-19 pandemic on mental health: Real-time surveillance using Google Trends. Psychological Trauma: Theory, Research, Practice, and Policy, 12(6), 567-568. https://psycnet.apa.org/doi/10.1037/tra0000872
  • Kristoufek, L., Moat, H. S., & Preis, T. (2016). Estimating suicide occurrence statistics using Google Trends. EPJ data science, 5, 1-12. http://dx.doi.org/10.1140/epjds/s13688-016-0094-0
  • Li, X., Zhou, M., Wu, J., Yuan, A., Wu, F., & Li, J. (2020). Analyzing COVID-19 on online social media: Trends, sentiments and emotions. ArXiv Preprint ArXiv:2005.14464. https://doi.org/10.48550/arXiv.2005.14464
  • Lwin, M. O., Lu, J., Sheldenkar, A., Schulz, P. J., Shin, W., Gupta, R., & Yang, Y. (2020). Global sentiments surrounding the COVID-19 pandemic on Twitter: analysis of Twitter trends. JMIR Public Health and Surveillance, 6(2), e19447. https://doi.org/10.2196/19447
  • Niederkrotenthaler, T., Fu, K. W., Yip, P. S., Fong, D. Y., Stack, S., Cheng, Q., & Pirkis, J. (2012). Changes in suicide rates following media reports on celebrity suicide: A meta-analysis. Journal of Epidemiol Community Health, 66(11), 1037-1042. http://dx.doi.org/10.1136/jech-2011-200707
  • Pfefferbaum, B., & North, C. S. (2020). Mental health and the Covid-19 pandemic. New England Journal of Medicine, 383(6), 510-512. https://doi.org/10.1056/NEJMp2008017
  • Rogers, J. P., Chesney, E., Oliver, D., Pollak, T. A., McGuire, P., Fusar-Poli, P., ... & David, A. S. (2020). Psychiatric and neuropsychiatric presentations associated with severe coronavirus infections: A systematic review and meta-analysis with comparison to the COVID-19 pandemic. The Lancet Psychiatry, 7(7), 611-627. https://doi.org/10.1016/S2215-0366(20)30203-0
  • Southwick, L., Guntuku, S. C., Klinger, E. V., Seltzer, E., McCalpin, H. J., & Merchant, R. M. (2021). Characterizing COVID-19 content posted to TikTok: public sentiment and response during the first phase of the COVID-19 pandemic. Journal of Adolescent Health, 69(2), 234-241. https://doi.org/10.1016/j.jadohealth.2021.05.010
  • Taquet, M., Geddes, J. R., Husain, M., Luciano, S., & Harrison, P. J. (2021). 6-month neurological and psychiatric outcomes in 236 379 survivors of COVID-19: A retrospective cohort study using electronic health records. The Lancet Psychiatry, 8(5), 416-427. https://doi.org/10.1371/journal.pmed.1003773
  • Ü. Dörttepe, Z., Hoşgör, H., & Sağcan, H. (2021). The effect of COVID-19 phobia on perceived stress: The sample of prehospital emergency care professionals. Journal of Academic Value Studies, 7(1), 31-40. https://doi.org/10.29228/javs.49250
  • Wang, C., Pan, R., Wan, X., Tan, Y., Xu, L., Ho, C. S., & Ho, R. C. (2020). Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China. International Journal of Environmental Research and Public Health, 17(5), 1729. https://doi.org/10.3390/ijerph17051729
  • Wang, J., Fan, Y., Palacios, J., Chai, Y., Guetta-Jeanrenaud, N., Obradovich, N., ... & Zheng, S. (2022). Global evidence of expressed sentiment alterations during the COVID-19 pandemic. Nature Human Behaviour, 6(3), 349-358. https://doi.org/10.1038/s41562-022-01312-y
  • Xiong, J., Lipsitz, O., Nasri, F., Lui, L. M., Gill, H., Phan, L., ... & McIntyre, R. S. (2020). Impact of COVID-19 pandemic on mental health in the general population: A systematic review. Journal of Affective Disorders, 277, 55-64. https://doi.org/10.1016/j.jad.2020.08.001
There are 21 citations in total.

Details

Primary Language Turkish
Subjects Behaviour-Personality Assessment in Psychology
Journal Section Articles
Authors

Derya Gündüz Hoşgör 0000-0002-1377-4617

Hacer Güngördü 0000-0003-3978-9259

Haydar Hoşgör 0000-0002-1174-1184

Project Number ---
Early Pub Date July 30, 2023
Publication Date July 31, 2023
Submission Date May 27, 2023
Acceptance Date July 5, 2023
Published in Issue Year 2023 Volume: 6 Issue: 3

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APA Gündüz Hoşgör, D., Güngördü, H., & Hoşgör, H. (2023). COVID-19 PANDEMİSİ VE ÖNCESİNDE HALKIN FARKLI DUYGU DURUM İFADELERİNE İLGİSİNİN GOOGLE TRENDLER ÜZERİNDEN ANALİZİ. R&S - Research Studies Anatolia Journal, 6(3), 267-282. https://doi.org/10.33723/rs.1303402
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