Global Tweet Mentions of COVID-19
Guangqing Chi, Junjun Yin, M. Luke Smith, and Yosef Bodovski

TL;DR
This paper introduces a comprehensive, open-source Twitter dataset related to COVID-19, enabling analysis of social dynamics, behavior, and policy impacts during the pandemic.
Contribution
It provides a large, validated dataset of geotagged COVID-19 related tweets with a dynamic dashboard for real-time monitoring and analysis.
Findings
88% accuracy in COVID-19 tweet classification
Decreasing correlation between tweets and diagnoses over time
Dataset useful for studying pandemic-related social dynamics
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMisinformation and Its Impacts · Data-Driven Disease Surveillance · Vaccine Coverage and Hesitancy
