{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Biobehavioral Health, The Pennsylvania State University, University Park, Pennsylvania, United States of America",
    "Institute for Computational and Data Sciences (ICDS), The Pennsylvania State University, University Park, Pennsylvania, United States of America",
    "Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, United States of America",
    "Department of Geography & Environmental Studies, University of New Mexico, Albuquerque, New Mexico, United States of America",
    "UNM Center for the Advancement of Spatial Informatics Research and Education (ASPIRE), University of New Mexico, Albuquerque, New Mexico, United States of America",
    "Department of Geography & the Environment, University of Denver, Denver, Colorado, United States of America"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xi Gong"
    },
    {
      "affs": [
        1
      ],
      "name": "Lin Liu"
    },
    {
      "affs": [
        2
      ],
      "name": "Yujian Lu"
    },
    {
      "affs": [
        3
      ],
      "name": "Guiming Zhang"
    },
    {
      "affs": [
        4
      ],
      "name": "Xiao Huang"
    },
    {
      "affs": [
        5
      ],
      "name": "Yan Lin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/anbdn0svq2ba4.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0356547",
  "fulltext_url": null,
  "id": "anbdn0svq2ba4",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0356547",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3205|Sleep and related disorders"
    ],
    [
      "plos",
      "/Biology and life sciences/Physiology/Physiological processes/Sleep"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Emotions"
    ],
    [
      "plos",
      "/Computer and information sciences/Information theory/Information entropy"
    ],
    [
      "plos",
      "/Computer and information sciences/Network analysis/Social networks/Social media"
    ],
    [
      "plos",
      "/Computer and information sciences/Network analysis/Social networks/Social media/Twitter"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Pandemics"
    ],
    [
      "plos",
      "/Medicine and health sciences/Medical conditions/Infectious diseases/Viral diseases/COVID 19"
    ],
    [
      "plos",
      "/Medicine and health sciences/Medical conditions/Sleep disorders/Dyssomnias/Insomnia"
    ],
    [
      "plos",
      "/Medicine and health sciences/Neurology/Sleep disorders/Dyssomnias/Insomnia"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Emotions"
    ],
    [
      "plos",
      "/Social sciences/Sociology/Communications/Social communication/Social media"
    ],
    [
      "plos",
      "/Social sciences/Sociology/Communications/Social communication/Social media/Twitter"
    ],
    [
      "plos",
      "/Social sciences/Sociology/Social networks/Social media"
    ],
    [
      "plos",
      "/Social sciences/Sociology/Social networks/Social media/Twitter"
    ]
  ],
  "openalex": "W7212080566",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0356547&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/anbdn0svq2ba4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:56Z",
      "native_id": "10.1371/journal.pone.0356547",
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      "url": "https://doi.org/10.1371/journal.pone.0356547"
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  "subjects": [
    {
      "field": "medicine",
      "origin": "plos:epidemiology",
      "subfield": "epidemiology-public-health"
    },
    {
      "field": "medicine",
      "origin": "plos:infectious diseases",
      "subfield": "infectious-disease"
    },
    {
      "field": "medicine",
      "origin": "plos:physiology",
      "subfield": "physiology"
    }
  ],
  "title": "Sleepless in America: A social sensing study of pandemic-era sleeplessness using nighttime social media data",
  "title_text": "Sleepless in America: A social sensing study of pandemic-era sleeplessness using nighttime social media data",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/anbdn0svq2ba4",
  "venue": "PLOS One",
  "versions": []
}