Fine-grained Geolocation of Tweets in Temporal Proximity

Abstract

In fine-grained tweet geolocation, tweets are linked to the specific venues (e.g., restaurants, shops) from which they were posted. This explicitly recovers the venue context that is essential for applications such as location-based advertising or user profiling. For this geolocation task, we focus on geolocating tweets that are contained in tweet sequences. In a tweet sequence, tweets are posted from some latent venue(s) by the same user and within a short time interval. This scenario arises from two observations: (1) It is quite common that users post multiple tweets in a short time and (2) most tweets are not geocoded. To more accurately geolocate a tweet, we propose a model that performs query expansion on the tweet (query) using two novel approaches. The first approach temporal query expansion considers users’ staying behavior around venues. The second approach visitation query expansion leverages on user revisiting the same or similar venues in the past. We combine both query expansion approaches via a novel fusion framework and overlay them on a Hidden Markov Model to account for sequential information. In our comprehensive experiments across multiple datasets and metrics, we show our proposed model to be more robust and accurate than other baselines.