Understanding Human Mobility from Twitter

By Raja Jurdak, Kun Zhao, Jiajun Liu, Maurice AbouJaoude, Mark A. Cameron, David Newth. PLoS ONE, 2014

405 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Epidemiology, Transportation

View Paper: https://www.semanticscholar.org/paper/c7b60647bb57bade97817c4296d51e9da81feeb7

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Citations in the corpus

Human mobility: Models and applications (2017, background): “Other measures such as visitation frequency and predictability are also obtainable [67].”
Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning (2017, background): “The GTSM data provide an unprecedented coverage of today’s major cities [22] and serve as a detailed proxy for understanding human activities [17, 5, 27, 18, 8, 26].”
A Survey on Deep Learning for Human Mobility (2020, background): “Examples of mobility data include tracks from GPS devices embedded in smartphones [23, 110, 127, 147, 238], vehicles [15, 63, 137, 140], or boats [32, 60, 154, 215]; records produced by the communication between phones and the cellular network [11, 20, 69, 199]; and geotagged posts from social media”