A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter

  1. Ari Z. Klein
  2. Arjun Magge
  3. Karen O’Connor
  4. Haitao Cai
  5. Davy Weissenbacher
  6. Graciela Gonzalez-Hernandez

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Abstract

The rapidly evolving outbreak of COVID-19 presents challenges for actively monitoring its spread. In this study, we assessed a social media mining approach for automatically analyzing the chronological and geographical distribution of users in the United States reporting personal information related to COVID-19 on Twitter. The results suggest that our natural language processing and machine learning framework could help provide an early indication of the spread of COVID-19.

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    SciScore for 10.1101/2020.04.19.20069948: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

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    Results from JetFighter: We did not find any issues relating to colormaps.

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    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.04.19.20069948v2 on medRxiv
  3. Version published to 10.1101/2020.04.19.20069948v1 on medRxiv