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.

睡眠不足与心脏病、糖尿病、癌症和事故增加有关，所有这些都是美国的主要死亡原因。人口规模的睡眠研究有可能通过帮助识别高危人群、集体睡眠模式的变化以及为政策变化提供信息来促进公共卫生。先前的研究表明，可以使用社交媒体活动来估计其他类型的健康指标，例如抑郁症和肥胖症。然而，无法使用公开数据有效测量集体睡眠限制了大规模的学术研究。在这里，我们通过对 Twitter 活动配置文件的代理分析来研究睡眠损失的被动估计。我们使用在美国夏令时开始时发生的“Spring Forward”事件，作为一种自然实验条件来估计美国各地睡眠不足的空间差异。平均而言，推特活动高峰发生在 Spring Forward 之后的周日 15 到 30 分钟后。然而，到周一早上，活动曲线与前一周重新对齐，这表明 Twitter 数据中的睡眠机会窗口被压缩，揭示了 Spring Forward 的行为变化。