Fine-grained location prediction of non geo-tagged tweets

Abstract

Geotagged Social Media (GTSM) data, especially geotagged tweets are valuable sources of information for many important applications. Only small portions of geotagged tweets are available (less than 3%). Identifying tweet location is a challenging problem that has attracted the interest of both academic and industry fields. Existing approaches have satisfactory accuracy at country and city level, but fail in locating more precisely the tweets. This paper presents FLAIR , an approach for geolocating tweets at finer granularities. Our objective is to predict the tweet location in a well-known and pre-defined area, that is to reduce the distance error between the predicted and real locations. In this work, we propose a location prediction model leveraging spatial model for POIs extracted from a text from one hand, and textual model comparing text similarity between geotagged and non-geotagged tweets, from another hand. We adopt a multi-view learning approach to combine the results of both predictions. Experimental results show that our proposed model outperforms the existing solutions.

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