Finding regions of interest using location based social media

The discovery of regions of interest in city groups is increasingly important in recent years. In this light, we propose and investigate a novel problem called Region Discovery query (RD query) that finds regions of interest with respect to a user's current geographic location. Given a set of spatial objects O and a query location q, if a circular region ω is with high spatial-object density and is spatially close to q, it is returned by the query and is recommended to users. This type of query can bring significant benefit to users in many useful applications such as trip planning and region recommendation. The RD query faces a big challenge: how to prune the search space in the spatial and density domains. To overcome the challenge and process the RD query efficiently, we propose a novel collaboration search method and we define a pair of bounds to prune the search space effectively. The performance of the RD query is studied by extensive experiments on real and synthetic spatial data

A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur

Proceedings of the Third Computing Women Congress (CWC 2008): Student papers

The Third Computing Women Congress was held at the University of Waikato, Hamilton, New Zealand from February 11th to 13th, 2008. The Computing Women Congress (CWC) is a Summer University for women in Computer Science. It is a meeting-place for female students, academics and professionals who study or work in Information Technology. CWC provides a forum to learn about and share the latest ideas of computing related topics in a supportive environment. CWC provides an open, explorative learning and teaching environment. Experimentation with new styles of learning is encouraged, with an emphasis on hands-on experience and engaging participatory techniques