Twitter as a Source of Global Mobility Patterns for Social Good
Social and Information Networks
2016-06-22 v1 Physics and Society
Machine Learning
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.
Cite
@article{arxiv.1606.06343,
title = {Twitter as a Source of Global Mobility Patterns for Social Good},
author = {Mark Dredze and Manuel García-Herranz and Alex Rutherford and Gideon Mann},
journal= {arXiv preprint arXiv:1606.06343},
year = {2016}
}
Comments
Presented at 2016 ICML Workshop on #Data4Good: Machine Learning in Social Good Applications, New York, NY