Web-a-where: geotagging web content
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic identification of user interest for personalized search
Proceedings of the 15th international conference on World Wide Web
Handbook of Approximation Algorithms and Metaheuristics (Chapman & Hall/Crc Computer & Information Science Series)
Towards automatic extraction of event and place semantics from flickr tags
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Spatial variation in search engine queries
Proceedings of the 17th international conference on World Wide Web
Exploring folksonomy for personalized search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 18th international conference on World wide web
Audience selection for on-line brand advertising: privacy-friendly social network targeting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploring social tagging graph for web object classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting user interests from contextual information
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
You are who you know: inferring user profiles in online social networks
Proceedings of the third ACM international conference on Web search and data mining
GeoFolk: latent spatial semantics in web 2.0 social media
Proceedings of the third ACM international conference on Web search and data mining
Find me if you can: improving geographical prediction with social and spatial proximity
Proceedings of the 19th international conference on World wide web
You are where you tweet: a content-based approach to geo-locating twitter users
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Geographical topic discovery and comparison
Proceedings of the 20th international conference on World wide web
Like like alike: joint friendship and interest propagation in social networks
Proceedings of the 20th international conference on World wide web
Scalable distributed inference of dynamic user interests for behavioral targeting
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Multiple location profiling for users and relationships from social network and content
Proceedings of the VLDB Endowment
Location prediction in social media based on tie strength
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Landmark-based user location inference in social media
Proceedings of the first ACM conference on Online social networks
Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles
ACM SIGOPS 24th Symposium on Operating Systems Principles
SPANStore: cost-effective geo-replicated storage spanning multiple cloud services
Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles
Kongress: a search and data mining application for U.S. congressional voting and Twitter data
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Account Reachability: A Measure of Privacy Risk for Exposure of a User's Multiple SNS Accounts
Proceedings of International Conference on Information Integration and Web-based Applications & Services
User profiling in an ego network: co-profiling attributes and relationships
Proceedings of the 23rd international conference on World wide web
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Users' locations are important to many applications such as targeted advertisement and news recommendation. In this paper, we focus on the problem of profiling users' home locations in the context of social network (Twitter). The problem is nontrivial, because signals, which may help to identify a user's location, are scarce and noisy. We propose a unified discriminative influence model, named as UDI, to solve the problem. To overcome the challenge of scarce signals, UDI integrates signals observed from both social network (friends) and user-centric data (tweets) in a unified probabilistic framework. To overcome the challenge of noisy signals, UDI captures how likely a user connects to a signal with respect to 1) the distance between the user and the signal, and 2) the influence scope of the signal. Based on the model, we develop local and global location prediction methods. The experiments on a large scale data set show that our methods improve the state-of-the-art methods by 13%, and achieve the best performance.