Twitter as a Source of Global Mobility Patterns for Social Good
This provides a novel data source for humanitarian and development efforts, though it is incremental as it applies an existing method to new data.
The paper tackled the problem of lacking data on human spatial distribution and movement by using geolocation data from Twitter to estimate global mobility patterns, addressing issues like accessibility, bias, and resolution.
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.