Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection

By Ryong Lee, Kazutoshi Sumiya. Workshop on Location-based Social Networks, 2010

332 citations recorded by the source corpus.

Subfields: Communication, Statistical and Nonlinear Physics, Transportation

View Paper: https://www.semanticscholar.org/paper/87a7ccc5f37cd846a978ae17d60b6dcd923bd996

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

1. Bo Xu
2. Linna Li
3. Michael F. Goodchild

In Statistical and Nonlinear Physics

1. Ross Maciejewski
2. Bo Xu
3. Linna Li
4. Michael F. Goodchild
5. Brent J. Hecht
6. Isaac L. Johnson
7. Johannes Schöning
8. Monica Stephens

In Transportation

1. Linna Li
2. Michael F. Goodchild
3. Brent J. Hecht
4. Isaac L. Johnson
5. Johannes Schöning
6. Monica Stephens

Citations in the corpus

• Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr (2013, background): “…rich data source, has been used to study people ’ s response to emergencies (Goodchild and Glennon 2010; Sakaki, Okazaki, and Matsuo 2010), the automatic detection of local events (Lee and Sumiya 2010), and predict election results based on the sentiments expressed in tweets (Tumasjan et al. 2010).”
• Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition (2012, background): “Special LBSN for certain domains, like Bikely1 and EveryTrail2 have an even stronger focus on the sharing and tracing of user locations.”