Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Accuracy of Hyper-local Geotagging of Social Media Content   Social media users share billions of items per year, only a small fraction of
which is geotagged. We present a data- driven approach for identifying
non-geotagged content items that can be associated with a hyper-local
geographic area by modeling the location distributions of hyper-local n-grams
that appear in the text. We explore the trade-off between accuracy, precision
and coverage of this method. Further, we explore differences across content
received from multiple platforms and devices, and show, for example, that
content shared via different sources and applications produces significantly
different geographic distributions, and that it is best to model and predict
location for items according to their source. Our findings show the potential
and the bounds of a data-driven approach to geotag short social media texts,
and offer implications for all applications that use data-driven approaches to
locate content.

20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th