SPOT

Abstract

A tremendous amount of information is being shared everyday on social media sites such as Facebook, Twitter or Google+. But only a small portion of users provide their location information, which can be helpful in targeted advertisement and many other services. In this demo we present our large scale user location estimation system, SPOT, which showcase different location estimating models on real world data sets. The demo shows three different location estimation algorithms: a friend-based, a social closeness-based, and an energy and local social coefficient based. The first algorithm is a baseline and the other two new algorithms utilize social closeness information which was traditionally treated as a binary friendship. The two algorithms are based on the premise that friends are different and close friends can help to estimate location better. The demo will also show that all three algorithms benefit from a confidence-based iteration method. The demo is web-based. A user can specify different settings, explore the estimation results on a map, and observe the statistical information, e.g. accuracy and average friends used in the estimation, dynamically. The demo provides two datasets: Twitter (148,860 located users) and Gowalla (99,563 located users). Furthermore, a user can filter users with certain features, e.g. with more than 100 friends, to see how the estimating models work on a particular case. The estimated and real locations of those users as well as their friends will be displayed on the map.

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