Twitter reveals human mobility dynamics during the COVID-19 pandemic

Abstract

The current COVID-19 pandemic raises concerns worldwide, leading to serious health, economic, and social challenges. The rapid spread of the virus at a global scale highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has proven to be associated with viral transmission. In this study, we analyzed over 580 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected from the user-generated information at the global, country, and U.S. state scale. Considering the multifaceted nature of mobility, we propose two types of distance: the single-day distance and the cross-day distance. To quantify the responsiveness in certain geographic regions, we further propose a mobility-based responsive index (MRI) that captures the overall degree of mobility changes within a time window. The results suggest that mobility patterns obtained from Twitter data are amenable to quantitatively reflect the mobility dynamics. Globally, the proposed two distances had greatly deviated from their baselines after March 11, 2020, when WHO declared COVID-19 as a pandemic. The considerably less periodicity after the declaration suggests that the protection measures have obviously affected people's travel routines. The country scale comparisons reveal the discrepancies in responsiveness, evidenced by the contrasting mobility patterns in different epidemic phases. We find that the triggers of mobility changes correspond well with the national announcements of mitigation measures, proving that Twitter-based mobility implies the effectiveness of those measures. In the U.S., the influence of the COVID-19 pandemic on mobility is distinct. However, the impacts vary substantially among states.

Fig 1. Conceptualization of single-day distance (D_sd) and cross-day distance (D_cd).

Fig 2. Mobility-based responsive index.

Fig 3. Temporal distribution of global D_sd and D_cd in the four-month period (February, March, April, and May).

Fig 4. Global NMI_sd (normalized D_sd) and NMI_cd (normalized D_cd) in the four-month period, and the monthly MRI for March, April, and May.

Fig 5. Temporal distribution of NMI_sd and NMI_cd for the top 20 countries with most Twitter users in February, March, April, and May.

Fig 6. Temporal distribution of NMI_sd and NMI_cd for states in CONUS (DC included; VT not included) in March, April, and May.

Fig 7. Temporal distribution of NMI_sd and NMI_cd for Minnesota.

Fig 8. Mobility-based responsive index (MRI) for CONUS states in March, April, May, and the difference between two consecutive months.

State boundaries are retrieved from the U.S. Census Bureau (https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html).