Random paper
 

Constructing a taxonomy of fine-grained human movement and activity motifs through social media

M. R. Frank, J. R. Williams, L. Mitchell, J. P. Bagrow, P. S. Dodds, and C. M. Danforth

Times cited: 2

Abstract:

Profiting from the emergence of web-scale social data sets, numerous recent studies have systematically explored human mobility patterns over large populations and large time scales. Relatively little attention, however, has been paid to mobility and activity over smaller time-scales, such as a day. Here, we use Twitter to identify people's frequently visited locations along with their likely activities as a function of time of day and day of week, capitalizing on both the content and geolocation of messages. We subsequently characterize people's transition pattern motifs and demonstrate that spatial information is encoded in word choice.
  • This is the default HTML.
  • You can replace it with your own.
  • Include your own code without the HTML, Head, or Body tags.

BibTeX:

@Misc{frank2014b,
  author = 	 {Frank, Morgan R. and Williams, Jake Ryland and Mitchell, Lewis and Bagrow, James P. and Dodds, Peter Sheridan and Danforth, Christopher M.},
  title = 	 {Constructing a taxonomy of fine-grained human movement and activity motifs through social media},
  year = 	 {2014},
  note = 	 {Preprint available at \href{https://arxiv.org/abs/1410.1393}{https://arxiv.org/abs/1410.1393}},
}

 

Random paper