On Fine-Grained Geolocalisation of Tweets

Abstract

Recently, the geolocalisation of tweets has become an important feature for a wide range of tasks in Information Retrieval and other domains, such as real-time event detection, topic detection or disaster and emergency analysis. However, the number of relevant geo-tagged tweets available remains insuffcient to reliably perform such tasks. Thus, predicting the location of non-geotagged tweets is an important yet challenging task, which can increase the sample of geo-tagged data and help to a wide range of tasks. In this paper, we propose a location inference method that utilises a ranking approach combined with a majority voting of tweets weighted based on the credibility of its source (Twitter user). Using geo-tagged tweets from two cities, Chicago and New York (USA), our experimental results demonstrate that our method (statistically) significantly outperforms our baselines in terms of accuracy, and error distance, in both cities, with the cost of decrease in recall.