GIS

This paper features Sindbad; a location-based social networking system. Sindbad supports three new services beyond traditional social networking services, namely, location-aware news feed, location-aware recommender, and location-aware ranking. These ...

Twitter has become one of the most popular platforms for sharing user-generated content, which varies from ordinary conversations to information about recent events. Studies have already showed that the content of tweets has a high degree of correlation ...

On-line photo sharing websites such as Flickr not only allow users to share their precious memories with others, they also act as a repository of all kinds of information carried by their photos and tags. The objective of this work is to perform ...

The availability of a huge amount of geotagged resources on the web can be exploited to extract new useful information. We propose a set of estimators that are able to evaluate the degree of clustering of the spatial distribution of terms used to tag ...

In the last decade, the Internet has seen the rise of social networking as the number one online activity worldwide. To estimate the geographical location of users of social networks at a particular moment, we propose an approach to geo-tag Twitter ...

These days the sharing of photographs and videos is very popular in social networks. Many of these social media websites such as Flickr, Facebook and Youtube allows the user to manually label their uploaded videos with geo-information using a interface ...

Understanding the spatial preference of mobile and web users is of great significance to creating and improving location-based recommendation systems, travel planners, search engines, and other emerging mobile applications. However, traditional sources ...

Artificial neural networks have been widely applied to spatial modeling and knowledge discovery because of their high-level intelligence and flexibility. Their highly parallel and distributed structure makes them inherently suitable for parallel ...

This paper presents a social network mining and analysis framework delivering personalized recommendations to the user in a privacy-preserving manner. Recommendations are based on the core elements of social media namely location, interests, work domain,...