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Understanding Human Mobility from Twitter
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4496063/ https://www.ncbi.nlm.nih.gov/pubmed/26154597 http://dx.doi.org/10.1371/journal.pone.0131469 |
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author | Jurdak, Raja Zhao, Kun Liu, Jiajun AbouJaoude, Maurice Cameron, Mark Newth, David |
author_facet | Jurdak, Raja Zhao, Kun Liu, Jiajun AbouJaoude, Maurice Cameron, Mark Newth, David |
author_sort | Jurdak, Raja |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4496063 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-44960632015-07-15 Understanding Human Mobility from Twitter Jurdak, Raja Zhao, Kun Liu, Jiajun AbouJaoude, Maurice Cameron, Mark Newth, David PLoS One Research Article 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. Public Library of Science 2015-07-08 /pmc/articles/PMC4496063/ /pubmed/26154597 http://dx.doi.org/10.1371/journal.pone.0131469 Text en © 2015 Jurdak et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Jurdak, Raja Zhao, Kun Liu, Jiajun AbouJaoude, Maurice Cameron, Mark Newth, David Understanding Human Mobility from Twitter |
title | Understanding Human Mobility from Twitter |
title_full | Understanding Human Mobility from Twitter |
title_fullStr | Understanding Human Mobility from Twitter |
title_full_unstemmed | Understanding Human Mobility from Twitter |
title_short | Understanding Human Mobility from Twitter |
title_sort | understanding human mobility from twitter |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4496063/ https://www.ncbi.nlm.nih.gov/pubmed/26154597 http://dx.doi.org/10.1371/journal.pone.0131469 |
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