Changes of Diurnal Rhythms of Social Media Activities during the COVID-19 Pandemic
Keywords:
COVID-19, pandemic, social media, circadian rhythmsAbstract
The COVID-19 pandemic has dramatically changed many aspects of our lives throughout the world. The Stay-at-Home orders imposed by the pandemic have exacerbated the stress and anxiety during the pandemic. To investigate how the social confinements affect people’s circadian rhythms at the population level, social activities on Twitter were analyzed for three different stages during the COVID-19 pandemic development. Results confirmed the diurnal rhythms of daily tweets before the Stay-at-Home orders, and also found that the diurnal rhythmicity of tweets was severely abolished during the Stay-at-Home orders, and then was restored after reopening. This study has a public health implication that circadian rhythms of the general public were significantly impacted by the social confinements during the COVID-19 pandemic, and circadian health education targeting the general population should be given enough attention and priority.
References
M. ten Thij, S. Bhulai, and P. Kampstra, “Circadian patterns in Twitter,” Data Anal. 2014, pp. 12–17, 2014.
F. Dzogang, S. Lightman, and N. Cristianini, “Circadian mood variations in Twitter content,” Brain Neurosci. Adv., vol. 1, p. 239821281774450, Jan. 2017.
E. Leypunskiy et al., “Geographically Resolved Rhythms in Twitter Use Reveal Social Pressures on Daily Activity Patterns,” Curr. Biol., vol. 28, no. 23, pp. 3763-3775.e5, Dec. 2018.
V. Lampos, T. Lansdall-Welfare, R. Araya, and N. Cristianini, “Analysing Mood Patterns in the United Kingdom through Twitter Content,” ArXiv, Apr. 2013.
N. Grinberg, M. Naaman, B. Shaw, and G. Lotan, “Extracting diurnal patterns of real world activity from social media,” Proc. 7th Int. Conf. Weblogs Soc. Media, ICWSM 2013, pp. 205–214, 2013.
T. Yasseri, R. Sumi, and J. Kertész, “Circadian Patterns of Wikipedia Editorial Activity: A Demographic Analysis,” PLoS One, vol. 7, no. 1, p. e30091, Jan. 2012.
S. A. Golder and M. W. Macy, “Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures,” Science (80-. )., vol. 333, no. 6051, pp. 1878–1881, Sep. 2011.
A. Noulas, S. Scellato, C. Mascolo, and M. Pontil, “An Empirical Study of Geographic User Activity Patterns in Foursquare,” Proc. Fifth Int. AAAI Conf. Weblogs Soc. Media, vol. 11, no. January, pp. 570–573, 2011.
J. M. Banda et al., “A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration,” ArXiv, Apr. 2020.
R. Muccari, D. Chow, and J. Murphy, “Coronavirus timeline: Tracking the critical moments of COVID-19,” NBCnews, Mar-2020.
R. Refinetti, G. Cornélissen, and F. Halberg, “Procedures for numerical analysis of circadian rhythms,” Biological Rhythm Research, vol. 38, no. 4. NIH Public Access, pp. 275–325, Aug-2007.
R. D. C. T. 3.5.1., “A Language and Environment for Statistical Computing,” R Foundation for Statistical Computing, vol. 2. R foundation for statistical computing Vienna, Austria, p. https://www.R-project.org, 2018.
J. Lai et al., “Factors Associated With Mental Health Outcomes Among Health Care Workers Exposed to Coronavirus Disease 2019,” JAMA Netw. open, vol. 3, no. 3, p. e203976, Mar. 2020.
C. Zhang et al., “Survey of Insomnia and Related Social Psychological Factors Among Medical Staff Involved in the 2019 Novel Coronavirus Disease Outbreak,” Front. Psychiatry, vol. 11, p. 1, Apr. 2020.
F. Beck, D. Léger, L. Fressard, P. Peretti‐Watel, and P. Verger, “Covid‐19 health crisis and lockdown associated with high level of sleep complaints and hypnotic uptake at the population level,” J. Sleep Res., Jun. 2020.
L. yu Lin et al., “The immediate impact of the 2019 novel coronavirus (COVID-19) outbreak on subjective sleep status,” Sleep Med., Jun. 2020.
N. Cellini, N. Canale, G. Mioni, and S. Costa, “Changes in sleep pattern, sense of time and digital media use during COVID‐19 lockdown in Italy,” J. Sleep Res., vol. 29, no. 4, Aug. 2020.
C. Blume, M. H. Schmidt, and C. Cajochen, “Effects of the COVID-19 lockdown on human sleep and rest-activity rhythms,” Current Biology, vol. 30, no. 14. Cell Press, pp. R795–R797, 20-Jul-2020.
K. P. Wright et al., “Sleep in university students prior to and during COVID-19 Stay-at-Home orders,” Current Biology, vol. 30, no. 14. Cell Press, pp. R797–R798, 20-Jul-2020.
M. A. Salehinejad et al., “Negative impact of the COVID-19 pandemic on sleep quantitative parameters, quality, and circadian alignment: Implications for psychological well-being and emotional regulation,” medRxiv, p. 2020.07.09.20149138, Jul. 2020.
M. Castaldo, T. Venturini, P. Frasca, and F. Gargiulo, “The Rhythms of the Night: increase in online night activity and emotional resilience during the Covid-19 lockdown,” ArXiv, Jul. 2020.
P. Gill, M. Arlitt, Z. Li, and A. Mahanti, “YouTube traffic characterization: A view from the edge,” in Proceedings of the ACM SIGCOMM Internet Measurement Conference, IMC, 2007, pp. 15–28.
G. Szabo and B. A. Huberman, “Predicting the popularity of online content,” Commun. ACM, vol. 53, no. 8, pp. 80–88, Nov. 2008.
Downloads
Published
How to Cite
Issue
Section
License
Authors who submit papers with this journal agree to the following terms.
