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Mental Health Interest and Its Prediction during the COVID-19 Pandemic Using Google Trends

Author

Listed:
  • Magdalena Sycińska-Dziarnowska

    (Department of Orthodontics, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland)

  • Liliana Szyszka-Sommerfeld

    (Department of Orthodontics, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland)

  • Karolina Kłoda

    (MEDFIT Karolina Kłoda, 71-050 Szczecin, Poland)

  • Michele Simeone

    (Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples “Federico II”, 80131 Napoli, Italy)

  • Krzysztof Woźniak

    (Department of Orthodontics, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland)

  • Gianrico Spagnuolo

    (Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples “Federico II”, 80131 Napoli, Italy
    Institute of Dentistry, I. M. Sechenov First Moscow State Medical University, 119435 Moscow, Russia)

Abstract

This study aimed to analyze and predict interest in mental health-related queries created in Google Trends (GT) during the COVID-19 pandemic. The Google Trends tool collected data on the Google search engine interest and provided real-time surveillance. Five key phrases: “depression”, “insomnia”, ”loneliness”, “psychologist”, and “psychiatrist”, were studied for the period from 25 September 2016 to 19 September 2021. The predictions for the upcoming trend were carried out for the period from September 2021 to September 2023 and were estimated by a hybrid five-component model. The results show a decrease of interest in the search queries “depression” and “loneliness” by 15.3% and 7.2%, respectively. Compared to the period under review, an increase of 5.2% in “insomnia” expression and 8.4% in the “psychiatrist” phrase were predicted. The expression “psychologist” is expected to show an almost unchanged interest. The upcoming changes in the expressions connected with mental health might be explained by vaccination and the gradual removal of social distancing rules. Finally, the analysis of GT can provide a timely insight into the mental health interest of a population and give a forecast for a short period trend.

Suggested Citation

  • Magdalena Sycińska-Dziarnowska & Liliana Szyszka-Sommerfeld & Karolina Kłoda & Michele Simeone & Krzysztof Woźniak & Gianrico Spagnuolo, 2021. "Mental Health Interest and Its Prediction during the COVID-19 Pandemic Using Google Trends," IJERPH, MDPI, vol. 18(23), pages 1-14, November.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:23:p:12369-:d:687116
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    References listed on IDEAS

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    1. Genevieve Belleville & Marie-Christine Ouellet & Charles M. Morin, 2019. "Post-Traumatic Stress among Evacuees from the 2016 Fort McMurray Wildfires: Exploration of Psychological and Sleep Symptoms Three Months after the Evacuation," IJERPH, MDPI, vol. 16(9), pages 1-14, May.
    2. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    3. Magdalena Sycinska-Dziarnowska & Iwona Paradowska-Stankiewicz & Krzysztof Woźniak, 2021. "The Global Interest in Vaccines and Its Prediction and Perspectives in the Era of COVID-19. Real-Time Surveillance Using Google Trends," IJERPH, MDPI, vol. 18(15), pages 1-11, July.
    4. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    5. Assimakopoulos, V. & Nikolopoulos, K., 2000. "The theta model: a decomposition approach to forecasting," International Journal of Forecasting, Elsevier, vol. 16(4), pages 521-530.
    6. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
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    Cited by:

    1. Yu Wang & Heming Deng & Sunan Gao & Tongxu Li & Feifei Wang, 2024. "A Fresh Perspective on Examining Population Emotional Well-Being Trends by Internet Search Engine: An Emerging Composite Anxiety and Depression Index," IJERPH, MDPI, vol. 21(2), pages 1-12, February.
    2. Nicholas Tze Ping Pang & Assis Kamu & Chong Mun Ho & Walton Wider & Mathias Wen Leh Tseu, 2022. "An Analysis by State on The Effect of Movement Control Order (MCO) 3.0 Due to COVID-19 on Malaysians’ Mental Health: Evidence from Google Trends," Data, MDPI, vol. 7(11), pages 1-9, November.

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