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
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Earthquake shakes Twitter users: real-time event detection by social sensors
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
Geographical topic discovery and comparison
Proceedings of the 20th international conference on World wide web
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A Novel Approach for Event Detection by Mining Spatio-temporal Information on Microblogs
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
"I'm eating a sandwich in Glasgow": modeling locations with tweets
Proceedings of the 3rd international workshop on Search and mining user-generated contents
Finding your friends and following them to where you are
Proceedings of the fifth ACM international conference on Web search and data mining
LARS: A Location-Aware Recommender System
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Proceedings of the 18th 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
Learning to rank for spatiotemporal search
Proceedings of the sixth ACM international conference on Web search and data mining
@Phillies Tweeting from Philly? Predicting Twitter User Locations with Spatial Word Usage
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Geo-spatial event detection in the twitter stream
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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Location profiles of user accounts in social media can be utilized for various applications, such as disaster warnings and location-aware recommendations. In this paper, we propose a scheme to infer users' home locations in social media. A large portion of existing studies assume that connected users (i.e., friends) in social graphs are located in close proximity. Although this assumption holds for some fraction of connected pairs, sometimes connected pairs live far from each other. To address this issue, we introduce a novel concept of landmarks, which are defined as users with a lot of friends who live in a small region. Landmarks have desirable features to infer users' home locations such as providing strong clues and allowing the locations of numerous users to be inferred using a small number of landmarks. Based on this concept, we propose a landmark mixture model (LMM) to infer users' location. The experimental results using a large-scale Twitter dataset show that our method improves the accuracy of the state-of-the-art method by about 27%.