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  1. Proceedings of the 5th ACM SIGSPATIAL International Workshop on Location-Based Social Networks (LBSN '12)
  2. Exploratory analysis on heterogeneous tag-point patterns for ranking and extracting hot-spot related tags
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The anatomy of Sindbad: a location-aware social networking system
Traffic observatory: a system to detect and locate traffic events and conditions using Twitter
TweoLocator: a non-intrusive geographical locator system for Twitter
Parallelization of ensemble neural networks for spatial land-use modeling
Can off-the-shelf object detectors be used to extract geographic information from geo-referenced social multimedia?
Multimodal geo-tagging in social media websites using hierarchical spatial segmentation
SNAIR: a framework for personalised recommendations based on social network analysis
Exploratory analysis on heterogeneous tag-point patterns for ranking and extracting hot-spot related tags
Public checkins versus private queries: measuring and evaluating spatial preference

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Exploratory analysis on heterogeneous tag-point patterns for ranking and extracting hot-spot related tags

Content Provider ACM Digital Library
Author Ruocco, Massimiliano Ramampiaro, Heri
Abstract The availability of a huge amount of geotagged resources on the web can be exploited to extract new useful information. We propose a set of estimators that are able to evaluate the degree of clustering of the spatial distribution of terms used to tag such geotagged resources. We introduce the concept of tag point pattern to derive indexes from the exploratory analysis by taking advantage of the second order Ripley's K-function, previously used in epidemiology, geo-statistics and ecology. The derived model estimates the degree of aggregation of the geotagged resources, taking into account the heterogeneity of the spatial distribution of the underlying population. Further, thanks to subsampling techniques, our approach is able to handle large datasets. Without losing of generality, we perform our experiments on a dataset derived Flickr pictures, as a use case. This dataset consists of tags that were extracted from a set of 1.2 million of pictures. We evaluate our proposed indexes with respect to their ability to extract tags related to geographical landmarks and hotspots. Our experiments show that we get good results using our estimators.
Starting Page 16
Ending Page 23
Page Count 8
File Format PDF
ISBN 9781450316989
DOI 10.1145/2442796.2442802
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2012-11-06
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Geographic information extraction Social media mining
Content Type Text
Resource Type Article
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