Sleepless in America: A social sensing study of pandemic-era sleeplessness using nighttime social media data
6 affiliations
- 1Department of Biobehavioral Health, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- 2Institute for Computational and Data Sciences (ICDS), The Pennsylvania State University, University Park, Pennsylvania, United States of America
- 3Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, United States of America
- 4Department of Geography & Environmental Studies, University of New Mexico, Albuquerque, New Mexico, United States of America
- 5UNM Center for the Advancement of Spatial Informatics Research and Education (ASPIRE), University of New Mexico, Albuquerque, New Mexico, United States of America
- 6Department of Geography & the Environment, University of Denver, Denver, Colorado, United States of America
Abstract
Sleeplessness is a widespread public health concern that was further intensified during the COVID-19 pandemic, yet large-scale, real-time monitoring of sleep disturbance remains limited. This study introduces a scalable social sensing framework that leverages temporally filtered nighttime social media activity (10:00 p.m. - 6:00 a.m. local time) to infer population-level patterns of sleep disturbance. To demonstrate the framework, we conducted a comprehensive analysis of geotagged tweets posted by users in the United States from March 1, 2020 to June 30, 2021, using pre-pandemic tweets as a baseline for comparison. Spatial, temporal, sentiment, and topical patterns were analyzed to characterize nighttime sleep disturbances at the U.S. state level. Three distinct temporal patterns of nighttime disturbance were identified during the pandemic, including single peak, multiple peaks, and smooth patterns, indicating heterogeneous impacts across states. Correlations between nighttime tweet ratios and daily new COVID-19 case counts varied across states and pandemic stages. In addition, more negative emotions expressed in nighttime tweets were associated with increased sleep disturbance. Nighttime tweets were more likely to focus on local news and events. Greater topical diversity among the population was associated with reduced negative emotional impacts during the pandemic. Overall, the findings demonstrate that nighttime social media data provide an effective and scalable social sensing approach for examining population-level patterns of potential sleep disturbance across large geographic regions and extended time periods. Beyond sleep-related applications, this framework offers a transferable method for monitoring other forms of nighttime human activity and broader social dynamics, including crime, disaster response, and social unrest.
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BibTeX
@article{gong2026sleepless,
title = {{Sleepless in America: A social sensing study of pandemic-era sleeplessness using nighttime social media data}},
author = {Xi Gong and Lin Liu and Yujian Lu and Guiming Zhang and Xiao Huang and Yan Lin},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0356547},
url = {https://doi.org/10.1371/journal.pone.0356547},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Sleepless in America: A social sensing study of pandemic-era sleeplessness using nighttime social media data
AU - Xi Gong
AU - Lin Liu
AU - Yujian Lu
AU - Guiming Zhang
AU - Xiao Huang
AU - Yan Lin
PY - 2026
DA - 2026/09/09
JO - PLOS One
DO - 10.1371/journal.pone.0356547
UR - https://doi.org/10.1371/journal.pone.0356547
AB - Sleeplessness is a widespread public health concern that was further intensified during the COVID-19 pandemic, yet large-scale, real-time monitoring of sleep disturbance remains limited. This study introduces a scalable social sensing framework that leverages temporally filtered nighttime social media activity (10:00 p.m. - 6:00 a.m. local time) to infer population-level patterns of sleep disturbance. To demonstrate the framework, we conducted a comprehensive analysis of geotagged tweets posted by users in the United States from March 1, 2020 to June 30, 2021, using pre-pandemic tweets as a baseline for comparison. Spatial, temporal, sentiment, and topical patterns were analyzed to characterize nighttime sleep disturbances at the U.S. state level. Three distinct temporal patterns of nighttime disturbance were identified during the pandemic, including single peak, multiple peaks, and smooth patterns, indicating heterogeneous impacts across states. Correlations between nighttime tweet ratios and daily new COVID-19 case counts varied across states and pandemic stages. In addition, more negative emotions expressed in nighttime tweets were associated with increased sleep disturbance. Nighttime tweets were more likely to focus on local news and events. Greater topical diversity among the population was associated with reduced negative emotional impacts during the pandemic. Overall, the findings demonstrate that nighttime social media data provide an effective and scalable social sensing approach for examining population-level patterns of potential sleep disturbance across large geographic regions and extended time periods. Beyond sleep-related applications, this framework offers a transferable method for monitoring other forms of nighttime human activity and broader social dynamics, including crime, disaster response, and social unrest.
SN - 1932-6203
ER -