The sleep loss insult of Spring Daylight Savings in the US is observable in Twitter activity
| Autor: | Peter Sheridan Dodds, Jeanie Lim, Michael Vincent Arnold, Thomas McAndrew, Christopher M. Danforth, Kelsey Linnell, Thayer Alshaabi |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer engineering. Computer hardware
medicine.medical_specialty Information Systems and Management Computer Networks and Communications Computer science Information technology TK7885-7895 Daylight Savings medicine Social media Twitter behavioral pattern Depression (differential diagnoses) Morning Public health Sociotechnical systems QA75.5-76.95 T58.5-58.64 medicine.disease Obesity Health indicator Hardware and Architecture Electronic computers. Computer science Sleep (system call) Sleep Daylight saving time Information Systems Demography |
| Zdroj: | Journal of Big Data, Vol 8, Iss 1, Pp 1-17 (2021) |
| ISSN: | 2196-1115 |
| DOI: | 10.1186/s40537-021-00503-0 |
| Popis: | 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 minutes 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink