Understanding Human Mobility from Twitter

Abstract

Understanding human mobility is crucial for a broad range of applications from disease prediction to communication networks. Most efforts on studying human mobility have so far used private and low resolution data, such as call data records. Here, we propose Twitter as a proxy for human mobility, as it relies on publicly available data and provides high resolution positioning when users opt to geotag their tweets with their current location. We analyse a Twitter dataset with more than six million geotagged tweets posted in Australia, and we demonstrate that Twitter can be a reliable source for studying human mobility patterns. Our analysis shows that geotagged tweets can capture rich features of human mobility, such as the diversity of movement orbits among individuals and of movements within and between cities. We also find that short and long-distance movers both spend most of their time in large metropolitan areas, in contrast with intermediate-distance movers movements, reflecting the impact of different modes of travel. Our study provides solid evidence that Twitter can indeed be a useful proxy for tracking and predicting human movement.

17 pages, 6 Figures

References in corpus (12)

• Understanding individual human mobility patterns
• The scaling laws of human travel
• Dynamics of person-to-person interactions from distributed RFID sensor networks
• Geo-located Twitter as the proxy for global mobility patterns
• Understanding the spreading patterns of mobile phone viruses
• Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data
• Contact patterns among high school students
• Characterizing Human Mobility Patterns in a Large Street Network
• Quantifying Human Mobility Perturbation and Resilience in Natural Disasters
• Social network dynamics of face-to-face interactions
• Inferring human mobility using communication patterns
• Towards a temporal network analysis of interactive WiFi users

Cited by in corpus (34)

• Human Mobility: Models and Applications
• Social physics
• Explore Spatiotemporal and Demographic Characteristics of Human Mobility via Twitter: A Case Study of Chicago
• Multi-scale spatio-temporal analysis of human mobility
• Immigrant community integration in world cities
• Human diffusion and city influence
• Collective motion of groups of self-propelled particles following interacting leaders
• Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems
• The Twitter Social Mobility Index: Measuring Social Distancing Practices from Geolocated Tweets
• On Predictability of Time Series
• Discovering and Characterizing Mobility Patterns in Urban Spaces: A Study of Manhattan Taxi Data
• Universal Components of Real-world Diffusion Dynamics based on Point Processes
• Analysing Human Mobility Patterns of Hiking Activities through Complex Network Theory
• Socioeconomic biases in urban mixing patterns of US metropolitan areas
• Understanding collective human movement dynamics during large-scale events using big geosocial data analytics
• Understanding the Loss in Community Resilience due to Hurricanes using Facebook Data
• Geolocated Social Media Posts are Happier: Understanding the Characteristics of Check-in Posts on Twitter
• Empirical Characterization of Mobility of Multi-Device Internet Users
• Where Chicagoans tweet the most: Semantic analysis of preferential return locations of Twitter users
• A Survey of Open Source User Activity Traces with Applications to User Mobility Characterization and Modeling
• ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Scale
• Diffusion in Colocation Contact Networks: the Impact of Nodal Spatiotemporal Dynamics
• Urban Mobility
• Spatio-Temporal Mobility Patterns of On-demand Ride-hailing Service Users
• Characterizing People's Daily Activity Patterns in the Urban Environment: A Mobility Network Approach with Geographic Context-Aware Twitter Data
• Towards real-time population estimates: introducing Twitter daily estimates of residents and non-residents at the county level
• Consistency of Social Sensing Signatures Across Major US Cities
• A New Understanding of Friendships in Space: Complex Networks Meet Twitter
• Measuring Global Multi-Scale Place Connectivity using Geotagged Social Media Data
• Clarifying the Role of Distance in Friendships on Twitter: Discovery of a Double Power-Law Relationship
• Diffusion on dynamic contact networks with indirect transmission links
• Fusion of Mobile Device Signal Data Attributes Enables Multi-Protocol Entity Resolution and Enhanced Large-Scale Tracking
• Tweeting Over The Border: An Empirical Study of Transnational Migration in San Diego and Tijuana
• From centre to centres: polycentric structures in individual mobility