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ISPRS Int. J. Geo-Inf. 2017, 6(2), 42; doi:10.3390/ijgi6020042

Discover Patterns and Mobility of Twitter Users—A Study of Four US College Cities

Libraries, Purdue University, 504 W State Street, West Lafayette, IN 47907, USA
Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall, West Lafayette, IN 47907, USA
Author to whom correspondence should be addressed.
Received: 21 November 2016 / Accepted: 29 January 2017 / Published: 10 February 2017
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Geo-tagged tweets provide useful implications for studies in human geography, urban science, location-based services, targeted advertising, and social network. This research aims to discover the patterns and mobility of Twitter users by analyzing the spatial and temporal dynamics in their tweets. Geo-tagged tweets are collected over a period of six months for four US Midwestern college cities: (1) West Lafayette, IN; (2) Bloomington, IN; (3) Ann Arbor, MI; (4) Columbus, OH. Various analytical and statistical methods are used to reveal the spatial and temporal patterns of tweets, and the tweeting behaviors of Twitter users. It is discovered that Twitter users are most active between 9:00 pm and 11:00 pm. In smaller cities, tweets aggregate at campuses and apartment complexes, while tweets in residential areas of bigger cities make up the majority of tweets. We also found that most Twitter users have two to four places of frequent visits. The mean mobility range of frequent Twitter users is linearly correlated to the size of the city, specifically, about 40% of the city radius. The research therefore confirms the feasibility and promising future for using geo-tagged microblogging services such as Twitter to understand human behavior patterns and carry out other geo-social related studies. View Full-Text
Keywords: spatial patterns; temporal patterns; human mobility; human dynamics; Twitter; social media spatial patterns; temporal patterns; human mobility; human dynamics; Twitter; social media

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, Y.; Li, Q.; Shan, J. Discover Patterns and Mobility of Twitter Users—A Study of Four US College Cities. ISPRS Int. J. Geo-Inf. 2017, 6, 42.

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