Learning to geolocalise Tweets at a fine-grained level

Fine-grained geolocation of tweets has become an important feature for reliably performing a wide range of tasks such as real-time event detection, topic detection or disaster and emergency analysis. Recent work adopted a ranking approach to return a predicted location based on content-based similarity to already available individual geotagged tweets. However, this work made use of the IDF weighting model to compute the ranking, which can diminish the quality of the Top-N retrieved tweets. In this work, we adopt a learning to rank approach towards improving the effectiveness of the ranking and increasing the accuracy of fine-grained geolocalisation. To this end we propose a set of features extracted from pairs of geotagged tweets generated within the same fine-grained geographical area (squared areas of size 1 km). Using geotagged tweets from two cities (Chicago and New York, USA), our experimental results show that our learning to rank approach significantly outperforms previous work based on IDF ranking, and improves accuracy of tweet geolocalisation at a fine-grained level.