Geo-located Twitter as the proxy for global mobility patterns (1311.0680v2)

Abstract: In the advent of a pervasive presence of location sharing services researchers gained an unprecedented access to the direct records of human activity in space and time. This paper analyses geo-located Twitter messages in order to uncover global patterns of human mobility. Based on a dataset of almost a billion tweets recorded in 2012 we estimate volumes of international travelers in respect to their country of residence. We examine mobility profiles of different nations looking at the characteristics such as mobility rate, radius of gyration, diversity of destinations and a balance of the inflows and outflows. The temporal patterns disclose the universal seasons of increased international mobility and the peculiar national nature of overseen travels. Our analysis of the community structure of the Twitter mobility network, obtained with the iterative network partitioning, reveals spatially cohesive regions that follow the regional division of the world. Finally, we validate our result with the global tourism statistics and mobility models provided by other authors, and argue that Twitter is a viable source to understand and quantify global mobility patterns.

• The paper demonstrates that geo-located Twitter data can reliably proxy global human mobility, with correlations up to R² = 0.88 against tourism metrics.
• It employs iterative network partitioning to identify spatially cohesive regions and distinguishes between resident and visitor travel flows.
• The study validates Twitter-derived mobility measures with traditional data, uncovering seasonal trends and highlighting demographic biases.

Analysis of Geo-located Twitter as a Proxy for Global Mobility Patterns

This paper provides an empirical analysis of geo-located Twitter data to reveal global patterns of human mobility. Utilizing a dataset comprising nearly one billion tweets from 2012, the paper seeks to discern international travel volumes with a focus on country-based mobility profiles. Through this exhaustive dataset, the researchers have measured indicators such as mobility rate, radius of gyration, diversity of destinations, and national inflows and outflows.

Methodological Approach

The authors employ iterative network partitioning to analyze the community structure of Twitter-based mobility networks, unveiling spatially cohesive regions aligned with the global regional divisions. This approach enables a comparison of mobility characteristics across various nations. Essential to their methodology is the assignment of users to a country of residence, which permits differentiation between residents and visitors—a crucial distinction for analyzing origin-destination flows.

Validation and Comparative Analysis

The paper validates Twitter-derived mobility data against global tourism statistics and established mobility models. Importantly, the results correlate strongly with traditional measures such as international tourist arrivals and financial receipts, achieving correlations of $R^2 = 0.69$ and $R^2 = 0.88$ respectively. The geographical mobility patterns observed through Twitter data exhibit similar statistical properties to those derived from other mobility datasets, conforming to power-law distributions for metrics like displacement probability.

Numerical Results and Inferences

Key findings include the fact that approximately 8% of all geo-located Twitter users were classified as mobile, indicating international travel. Notably, the United States exhibited surprisingly low mobility rates relative to its Twitter penetration, contrasting with nations such as Singapore and Kuwait that demonstrated high mobility and penetration rates. The analysis identifies seasonal mobility patterns, with significant increases during summer and the year-end holiday season.

Implications for Future Research

The outcomes suggest that geo-located Twitter data serves as a viable proxy for studying global mobility, particularly at a country-to-country interaction level. This methodology could potentially extend to examining finer spatial scales. Despite possible biases toward specific demographic segments, Twitter's global reach and ease of access render it a useful tool for mobility research. The findings also validate that human travel behaviors, even in an era of globalization, are still dominantly regional, aligning with established socio-economic areas.

Future Directions

The paper's approach could inspire subsequent research efforts to explore more granular mobility patterns and the dynamics of urban movement using Twitter data. As data availability continues to expand alongside technological advancements, there is potential for employing similar methodologies across other social media platforms for comprehensive mobility analyses. Additionally, refining models to address demographic biases inherent in social media datasets will enhance the robustness and reliability of such studies.

In conclusion, this paper makes a significant contribution to the understanding of global human mobility patterns through the innovative use of geo-located Twitter data, highlighting the potential of social media as a valuable source in mobility research.