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Detecting non-gaussian geographical topics in tagged photo collections

Detecting non-gaussian geographical topics in tagged photo collections
Detecting non-gaussian geographical topics in tagged photo collections
Nowadays, large collections of photos are tagged with GPS coordinates. The modelling of such large geo-tagged corpora is an important problem in data mining and information retrieval, and involves the use of geographical information to detect topics with a spatial component. In this paper, we propose a novel geographical topic model which captures dependencies between geographical regions to support the detection of topics with complex, non-Gaussian distributed spatial structures. The model is based on a multi-Dirichlet process (MDP), a novel generalisation of the hierarchical Dirichlet process extended to support multiple base distributions. Our method thus is called the MDP-based geographical topic model (MGTM). We show how to use a MDP to dynamically smooth topic distributions between groups of spatially adjacent documents. In systematic quantitative and qualitative evaluations using independent datasets from prior related work, we show that such a model can exploit the adjacency of regions and leads to a significant improvement in the quality of topics compared to the state of the art in geographical topic modelling.
603-612
ACM
Kling, Christoph Carl
3e66e481-5a13-444f-87e2-772268e9fdb1
Kunegis, Jérôme
066b7173-f5a6-4a0e-9656-873af0821799
Sizov, Sergej
ecc519ba-5393-4290-8441-b7e2a780444e
Staab, Steffen
bf48d51b-bd11-4d58-8e1c-4e6e03b30c49
Kling, Christoph Carl
3e66e481-5a13-444f-87e2-772268e9fdb1
Kunegis, Jérôme
066b7173-f5a6-4a0e-9656-873af0821799
Sizov, Sergej
ecc519ba-5393-4290-8441-b7e2a780444e
Staab, Steffen
bf48d51b-bd11-4d58-8e1c-4e6e03b30c49

Kling, Christoph Carl, Kunegis, Jérôme, Sizov, Sergej and Staab, Steffen (2014) Detecting non-gaussian geographical topics in tagged photo collections. In WSDM '14 Proceedings of the 7th ACM international conference on Web search and data mining. ACM. pp. 603-612 . (doi:10.1145/2556195.2556218).

Record type: Conference or Workshop Item (Paper)

Abstract

Nowadays, large collections of photos are tagged with GPS coordinates. The modelling of such large geo-tagged corpora is an important problem in data mining and information retrieval, and involves the use of geographical information to detect topics with a spatial component. In this paper, we propose a novel geographical topic model which captures dependencies between geographical regions to support the detection of topics with complex, non-Gaussian distributed spatial structures. The model is based on a multi-Dirichlet process (MDP), a novel generalisation of the hierarchical Dirichlet process extended to support multiple base distributions. Our method thus is called the MDP-based geographical topic model (MGTM). We show how to use a MDP to dynamically smooth topic distributions between groups of spatially adjacent documents. In systematic quantitative and qualitative evaluations using independent datasets from prior related work, we show that such a model can exploit the adjacency of regions and leads to a significant improvement in the quality of topics compared to the state of the art in geographical topic modelling.

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More information

e-pub ahead of print date: 24 February 2014
Published date: February 2014
Venue - Dates: 7th ACM International Conference on Web Search and Data Mining (WSDM '14), United States, 2014-02-24 - 2014-02-28

Identifiers

Local EPrints ID: 413597
URI: https://eprints.soton.ac.uk/id/eprint/413597
PURE UUID: 69dde4f2-c0e2-43aa-897c-119aa1e95f8c
ORCID for Steffen Staab: ORCID iD orcid.org/0000-0002-0780-4154

Catalogue record

Date deposited: 30 Aug 2017 16:31
Last modified: 14 Mar 2019 01:32

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