The sleep loss insult of Spring Daylight Savings in the US is observable in Twitter activity

Journal of Big Data

Christopher M. Danforth¹  Thayer Alshaabi¹  Peter Sheridan Dodds¹  Michael Arnold¹  Kelsey Linnell¹  Thomas McAndrew²  Jeanie Lim³ 

[1] Computational Story Lab, Vermont Complex Systems Center, MassMutual Center of Excellence for Complex Systems & Data Science, University of Vermont, Burlington, USA;Lehigh University, Bethlehem, USA;MassMutual Data Science, Amherst, USA;

关键词: Sleep;    Sociotechnical systems;    Daylight Savings;    Twitter;    Public health;    Twitter behavioral pattern;   

DOI  :  10.1186/s40537-021-00503-0

来源: Springer

【 摘 要 】

Sleep loss has been linked to heart disease, diabetes, cancer, and an increase in accidents, all of which are among the leading causes of death in the United States. Population-scale sleep studies have the potential to advance public health by helping to identify at-risk populations, changes in collective sleep patterns, and to inform policy change. Prior research suggests other kinds of health indicators such as depression and obesity can be estimated using social media activity. However, the inability to effectively measure collective sleep with publicly available data has limited large-scale academic studies. Here, we investigate the passive estimation of sleep loss through a proxy analysis of Twitter activity profiles. We use “Spring Forward” events, which occur at the beginning of Daylight Savings Time in the United States, as a natural experimental condition to estimate spatial differences in sleep loss across the United States. On average, peak Twitter activity occurs 15 to 30 min later on the Sunday following Spring Forward. By Monday morning however, activity curves are realigned with the week before, suggesting that the window of sleep opportunity is compressed in Twitter data, revealing Spring Forward behavioral change.

【 授权许可】

CC BY