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Anxiety, gender, and social media consumption predict COVID-19 emotional distress

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  • Joseph Heffner

    (Brown University)

  • Marc-Lluís Vives

    (Brown University)

  • Oriel FeldmanHall

    (Brown University
    Brown University)

Abstract

Fear and anxiety about COVID-19 have swept across the globe. Understanding the factors that contribute to increased emotional distress regarding the pandemic is paramount—especially as experts warn about rising cases. Despite large amounts of data, it remains unclear which variables are essential for predicting who will be most affected by the distress of future waves. We collected cross-sectional data on a multitude of socio-psychological variables from a sample of 948 United States participants during the early stages of the pandemic. Using a cross-validated hybrid stepwise procedure, we developed a descriptive model of COVID-19 emotional distress. Results reveal that trait anxiety, gender, and social (but not government) media consumption were the strongest predictors of increasing emotional distress. In contrast, commonly associated variables, such as age and political ideology, exhibited much less unique explanatory power. Together, these results can help public health officials identify which populations will be especially vulnerable to experiencing COVID-19-related emotional distress.

Suggested Citation

  • Joseph Heffner & Marc-Lluís Vives & Oriel FeldmanHall, 2021. "Anxiety, gender, and social media consumption predict COVID-19 emotional distress," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-7, December.
    • Handle: RePEc:pal:palcom:v:8:y:2021:i:1:d:10.1057_s41599-021-00816-8
    DOI: 10.1057/s41599-021-00816-8
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    References listed on IDEAS

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    1. Anton Gollwitzer & Cameron Martel & William J. Brady & Philip Pärnamets & Isaac G. Freedman & Eric D. Knowles & Jay J. Van Bavel, 2020. "Partisan differences in physical distancing are linked to health outcomes during the COVID-19 pandemic," Nature Human Behaviour, Nature, vol. 4(11), pages 1186-1197, November.
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    Cited by:

    1. Fulian Yin & Meiqi Ji & Zhongliang Yang & Zhaoliang Wu & Xinyu Xia & Tongtong Xing & Yuwei She & Zhiwen Hu, 2022. "Exploring the determinants of global vaccination campaigns to combat COVID-19," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-13, December.
    2. Guglielmo Bonaccorsi & Veronica Gallinoro & Andrea Guida & Chiara Morittu & Valerio Ferro Allodola & Vieri Lastrucci & Patrizio Zanobini & Orkan Okan & Kevin Dadaczynski & Chiara Lorini, 2023. "Digital Health Literacy and Information-Seeking in the Era of COVID-19: Gender Differences Emerged from a Florentine University Experience," IJERPH, MDPI, vol. 20(3), pages 1-12, January.
    3. Zhihang Liu & Jinlin Wu & Connor Y. H. Wu & Xinming Xia, 2024. "Shifting sentiments: analyzing public reaction to COVID-19 containment policies in Wuhan and Shanghai through Weibo data," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    4. Ofrit Kol & Dorit Zimand-Sheiner & Shalom Levy, 2024. "A tale of two paths to vaccine acceptance: self-interest and collective interest effect, mediated by institutional trust, and moderated by gender," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-8, December.
    5. Boyang Shi & Weixiang Huang & Yuanyuan Dang & Wenhui Zhou, 2024. "Leveraging social media data for pandemic detection and prediction," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-18, December.

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