Twitter as a Source of Global Mobility Patterns for Social Good
arXiv:1606.06343
Abstract
Data on human spatial distribution and movement is essential for understanding and analyzing social systems. However existing sources for this data are lacking in various ways; difficult to access, biased, have poor geographical or temporal resolution, or are significantly delayed. In this paper, we describe how geolocation data from Twitter can be used to estimate global mobility patterns and address these shortcomings. These findings will inform how this novel data source can be harnessed to address humanitarian and development efforts.
arXiv commentPresented at 2016 ICML Workshop on #Data4Good: Machine Learning in Social Good Applications, New York, NY
References in corpus (6)
- Multiscale mobility networks and the large scale spreading of infectious diseases
- Prediction and predictability of global epidemics: the role of the airline transportation network
- Geo-located Twitter as the proxy for global mobility patterns
- The Twitter of Babel: Mapping World Languages through Microblogging Platforms
- Diffusion of Lexical Change in Social Media
- Geotagging One Hundred Million Twitter Accounts with Total Variation Minimization