2017
DOI: 10.1109/jproc.2017.2688799
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Geotagging Text Content With Language Models and Feature Mining

Abstract: The large-scale availability of user-generated content in social media platforms has recently opened up new possibilities for studying and understanding the geospatial aspects of real-world phenomena and events. Yet, the large majority of user-generated content lacks proper geographic information (in the form of latitude and longitude coordinates). As a result, the problem of multimedia geotagging, i.e. extracting location information from user-generated text items when this is not explicitly available, has at… Show more

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Cited by 30 publications
(25 citation statements)
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“…Recent language modeling-based approaches have given better results. Among these, Kordopatis-Zilos et al [27] created a rectangular grid of cells and generated term-cell probabilities. The term occurrence probabilities are calculated from processing a geotagged corpus.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Recent language modeling-based approaches have given better results. Among these, Kordopatis-Zilos et al [27] created a rectangular grid of cells and generated term-cell probabilities. The term occurrence probabilities are calculated from processing a geotagged corpus.…”
Section: Discussionmentioning
confidence: 99%
“…Kordopatis-Zilos et al [27] presented an approach for estimating locations using text annotations based on refined language models that are learned from massive corpora of social media annotations. They also explored the impact of different feature selection and weighting techniques on the performance of the approach.…”
Section: Related Workmentioning
confidence: 99%
“…The latter may achieve high precision for the geo-localization of users at a country level, or even within country regions or cities [40,41]. However, to achieve a good performance at a finer grain classification, such as commune/neighborhood level, massive corpora of social media annotation is required [37]. On the other hand, the geo-localization of users based on their network (based on the assumption that users are more likely to interact with other users that are geographically closer to them) are more accurate at a finer level [42,43].…”
Section: Geolocation Of the Followersmentioning
confidence: 99%
“…We inferred the location of posts, even in cases when it was not explicitly available through the geotagging metadata accompanying a tweet. Geolocation inference from text was based on the approach by Kordopatis-Zilos et al (2017), which employs refined language models learned from massive corpora of social media annotations. The results of the geolocation extraction for the posts of suspended and non-suspended users are presented in Figs.…”
Section: Spatial Distribution Of Accountsmentioning
confidence: 99%