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.

Public Health Messaging in an Era of Social Media

“…A study of 580 million tweets posted during the early months (ie, January to May 2020) of the pandemic showed that the geographic information associated with tweets can be used as a proxy for human mobility. 4 Public health organizations can use this realtime data to inform messaging content and where to target hyperlocal messages about public health measures (eg, shelter in place).…”

Spatial-temporal relationship between population mobility and COVID-19 outbreaks in South Carolina: A time series forecasting analysis

“…Population mobility was assessed using the number of people (Twitter users) with moving distance larger than 0.5 mile per day in SC and the selected counties. The methodology of extracting daily population movement (origin-destination flows) from geotagged tweets is discussed elsewhere [18,19]. Briefly, geotagged tweets during the study periods were collected and used for calculation.…”

“…Daily travel distance was calculated for each user based on the derived origin-destination flows and used to generate a variable of how many people moved each day (with travel distance larger than 0.5 mile). This method of capturing population mobility through Twitter has been previously validated [18].…”

A Bibliometric Analysis and Network Visualisation of Human Mobility Studies from 1990 to 2020: Emerging Trends and Future Research Directions

“…Third, a more recent data source is the data retrieved from the increasingly popular social media applications (e.g., Facebook and Twitter). Such social media data contain spatial and temporal information obtained from users when they choose to post contents with locations, generating diverse index-based mobility data at the aggregated levels, such as the mobilitybased responsive index (Huang et al, 2020;Jurdak et al, 2015). Future studies need to put efforts to compare the quality of these multi-source mobility data and explore the possible replacement of data to improve data availability to public and researchers.…”