Global Tweet Mentions of COVID-19

• URL: http://arxiv.org/abs/2108.06385v3
• Date: Wed, 18 Aug 2021 02:24:50 GMT
• Title: Global Tweet Mentions of COVID-19
• Authors: Guangqing Chi, Junjun Yin, M. Luke Smith, and Yosef Bodovski
• Abstract summary: We present an open-source dataset of 1.92 million keyword-selected Twitter posts, updated weekly from January 2020 to present. The dashboard presents 100% of the geotagged tweets that contain keywords or hashtags related COVID-19. With emerging COVID variants but ongoing vaccine hesitancy and resistance, this dataset could be used by researchers to study numerous aspects of COVID-19.
• Score: 3.3043776328952226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background. After a year and half and over 4 million deaths, the COVID-19 pandemic continues to be widespread, and its related topics continue to dominate the global media. Although COVID-19 diagnoses have been well monitored, neither the impacts of the disease on human behavior and social dynamics nor the effectiveness of policy interventions aimed at its containment are fully understood. Monitoring the spatial and temporal patterns of behavior, social dynamics and policy - and then their interrelations - can provide critical information for preparatory action and effective response. Methods. Here we present an open-source dataset of 1.92 million keyword-selected Twitter posts, updated weekly from January 2020 to present, along with a dynamic dashboard showing totals at national and subnational administrative divisions. Results. The dashboard presents 100% of the geotagged tweets that contain keywords or hashtags related COVID-19. We validated our inclusion criteria using a machine learning-based text classifier and found that 88% of the selected tweets were correctly labeled as related to COVID-19. With this information we tested the correlation between tweets and covid diagnosis from January 1, 2020 through December 31, 2020 and see a decreasing correlation across time. Conclusions. With emerging COVID variants but ongoing vaccine hesitancy and resistance, this dataset could be used by researchers to study numerous aspects of COVID-19 and provide valuable insights for preparing future pandemics.

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