A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Spatial variation in search engine queries
Proceedings of the 17th international conference on World Wide Web
Placing flickr photos on a map
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
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
Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Finding locations of flickr resources using language models and similarity search
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Automatic tagging and geotagging in video collections and communities
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Multi-modal, multi-resource methods for placing Flickr videos on the map
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Proceedings of the 20th ACM international conference on Information and knowledge management
"I'm eating a sandwich in Glasgow": modeling locations with tweets
Proceedings of the 3rd international workshop on Search and mining user-generated contents
Object matching in tweets with spatial models
Proceedings of the fifth ACM international conference on Web search and data mining
Steeler nation, 12th man, and boo birds: classifying Twitter user interests using time series
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Location-specific tweet detection and topic summarization in Twitter
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Landmark-based user location inference in social media
Proceedings of the first ACM conference on Online social networks
Inferring the origin locations of tweets with quantitative confidence
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
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We study the problem of predicting home locations of Twitter users using contents of their tweet messages. Using three probability models for locations, we compare both the Gaussian Mixture Model (GMM) and the Maximum Likelihood Estimation (MLE). In addition, we propose two novel unsupervised methods based on the notions of Non-Localness and Geometric-Localness to prune noisy data from tweet messages. In the experiments, our unsupervised approach improves the baselines significantly and shows comparable results with the supervised state-of-the-art method. For 5,113 Twitter users in the test set, on average, our approach with only 250 selected local words or less is able to predict their home locations (within 100 miles) with the accuracy of 0.499, or has 509.3 miles of average error distance at best.