Geotagging Text Content With Language Models and Feature Mining

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 attracted increasing research interest. Here, we present a highly accurate geotagging approach for estimating the locations alluded by text annotations based on refined language models that are learned from massive corpora of social media annotations. We further explore the impact of different feature selection and weighting techniques on the performance of the approach. In terms of evaluation, we employ a large benchmark collection from the MediaEval Placing Task over several years. We demonstrate the consistently superior geotagging accuracy and low median distance error of the proposed approach using various data sets and comparing it against a number of state-of-the-art systems.