2015
DOI: 10.1007/978-3-319-18455-5_2
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Geotagging Social Media Content with a Refined Language Modelling Approach

Abstract: Abstract. The problem of content geotagging, i.e. estimating the geographic position of a piece of content (text message, tagged image, etc.) when this is not explicitly available, has attracted increasing interest as the large volumes of user-generated content posted through social media platforms such as Twitter and Instagram form nowadays a key element in the coverage of news stories and events. In particular, in large-scale incidents, where location is an important factor, such as natural disasters and ter… Show more

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Cited by 11 publications
(5 citation statements)
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“…The authors used smoothing techniques for refining the cell prediction. In another study, Kordopatis -Zilos et al [9] introduced a bag of tags method that the probability of a tag being used by users is determined for describing a region. The tags in each cell are weighted based on spatial entropy that the tags which are user specific or general are assigned less weight.…”
Section: B Automatic Gazetteer Expansion or Enrichmentmentioning
confidence: 99%
“…The authors used smoothing techniques for refining the cell prediction. In another study, Kordopatis -Zilos et al [9] introduced a bag of tags method that the probability of a tag being used by users is determined for describing a region. The tags in each cell are weighted based on spatial entropy that the tags which are user specific or general are assigned less weight.…”
Section: B Automatic Gazetteer Expansion or Enrichmentmentioning
confidence: 99%
“…The remaining terms are then ranked and filtered on the basis of three measures: accuracy, spatial entropy and locality. 1) Accuracy: This was originally proposed in [23] as a means of quantifying the geotagging capability of terms and correlating their occurrence with correct location estimates. To calculate the accuracy of a term t ∈ T, a scheme similar to cross-validation is employed.…”
Section: Feature Selectionmentioning
confidence: 99%
“…As a result, the precision at low granularities improved almost 10 times. More details on the employed methods are presented in Sections III-B and III-E. • Extending previous approaches that use feature selection during the LM construction step, such as our previous approach [23], we propose a more versatile, scalable and powerful feature selection and weighting scheme, which leads to considerable improvement in terms of geotagging accuracy and to increased resilience with respect to the training dataset. In particular, the impact of feature selection is the reduction of median distance error up to ≈88%.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Blessing et al [20] used named entity recognition (NER) and a knowledgebase gazetteer to recognize and locate the geographic names for German content. Kordopatis-Zilos et al [21], [22] refined language models for geotagging social media content based on text. Various geotagging tasks for different types of digital resources have been conducted in an international benchmarking initiative named MediaEval [23].…”
Section: Introductionmentioning
confidence: 99%