Name
Abstract | ||
|---|---|---|
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 p.m. and 11:00 p.m. 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3390/ijgi6020042 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION |
Keywords | Field | DocType |
spatial patterns,temporal patterns,human mobility,human dynamics,Twitter,social media | Apartment,Internet privacy,Social media,Social network,Human geography,Microblogging,Human dynamics,Targeted advertising,Geography | Journal |
Volume | Issue | Citations |
6 | 2 | 4 |
PageRank | References | Authors |
0.43 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Li | 1 | 6 | 0.81 |
| Qing-Hua Li | 2 | 1563 | 88.15 |
| J. Shan | 3 | 220 | 20.08 |