Matrix Factorization for Near Real-time Geolocation Prediction in Twitter Stream

The geographical location is vital to geospatial applications such as event detection, geo-aware recommendation and local search. Previous research on this topic has investigated geolocation prediction framework via conducting pre-partitioning and applying classification methods. These existing approaches target user’s geolocation all at once via concatenation of tweets. In this paper, we study a novel problem in geolocation. We aim to predict user’s geolocation at a given tweet’s posting time. We propose a geo matrix factorization model to address this problem. First, we map tweets into a latent space using a matrix factorization technique. Second, we use a linear combination in the latent space to predict exact latitude and longitude. However, we only use one individual tweet as the input instead of using a concatenation of all tweets of a user. Our experimental results show that the proposed model has outperformed a set of regression models and state-of-the-art classification approaches.