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Discover Computing
Erschienen in: Discover Computing 1/2013

01.02.2013

Modeling locations with social media

verfasst von: Neil O’Hare, Vanessa Murdock

Erschienen in: Discover Computing | Ausgabe 1/2013

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Abstract

In this paper we focus on the locations explicit and implicit in users descriptions of their surroundings. We propose a statistical language modeling approach to identifying locations in arbitrary text, and investigate several ways to estimate the models, based on the term frequency and the user frequency. The geotagged public photos in Flickr serve as a convenient ground truth. Our results show that we can predict location within a one kilometer by one kilometer cell with 17 % accuracy, and within a three kilometer radius around such a one kilometer cell with 40 % accuracy, using only a photo’s tags. This is significantly better than the state of the art. Further we examine several estimation strategies that leverage the physical proximity of places, and show that for sparsely represented locations, smoothing from the immediate neighborhood improves results. We also show that estimation strategies based on user frequency are much more reliable than approaches based on the raw term frequency.
Vorheriger Artikel Leveraging comparable corpora for cross-lingual information retrieval in resource-lean language pairs
Nächster Artikel Balancing exploration and exploitation in listwise and pairwise online learning to rank for information retrieval

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Fußnoten
1
http://​www.​flickr.​com visited March 2011.
 
2
http://​www.​geonames.​org/​ visited January 2012.
 
5
http://​www.​navteq.​com/​ visited January 2012.
 
6
http://​www.​teleatlas.​com visited January 2012.
 
8
Note that Flickr has a public API which allows members of the research community to download metadata and images from the public photos of users. http://​www.​flickr.​com/​services/​api/​ visited January 2012.
 
9
We do not present the complete set of results for the small dataset for all hierarchical smoothing approaches here (for brevity), but the relative performance of the different approaches is the similar to those in Table 4.
 
10
Personal communication with the creators of the CoPhIR dataset. The removed archive was sapir_id_1_xml_r.tgz.
 
12
http://​www.​facebook.​com visited January 2012.
 
13
http://​www.​twitter.​com visited January 2012.
 
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Metadaten
Titel
Modeling locations with social media
verfasst von
Neil O’Hare
Vanessa Murdock
Publikationsdatum
01.02.2013
Verlag
Springer Netherlands
Erschienen in
Discover Computing / Ausgabe 1/2013
Print ISSN: 2948-2984
Elektronische ISSN: 2948-2992
DOI
https://doi.org/10.1007/s10791-012-9195-y

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