Extended Boolean information retrieval
Communications of the ACM
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Location disclosure to social relations: why, when, & what people want to share
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
A probabilistic approach to spatiotemporal theme pattern mining on weblogs
Proceedings of the 15th international conference on World Wide Web
Context-aware telephony: privacy preferences and sharing patterns
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
A comparison of methods for the automatic identification of locations in wikipedia
Proceedings of the 4th ACM workshop on Geographical information retrieval
From awareness to repartee: sharing location within social groups
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Spatial variation in search engine queries
Proceedings of the 17th international conference on World Wide Web
Seeing our signals: combining location traces and web-based models for personal discovery
Proceedings of the 9th workshop on Mobile computing systems and applications
Proceedings of the 18th international conference on World wide web
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Power-Law Distributions in Empirical Data
SIAM Review
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
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
Bridging the gap between physical location and online social networks
Proceedings of the 12th ACM international conference on Ubiquitous computing
A latent variable model for geographic lexical variation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
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
Simple supervised document geolocation with geodesic grids
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Part-of-speech tagging for Twitter: annotation, features, and experiments
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Exploiting geographical influence for collaborative point-of-interest recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Who's your best friend?: targeted privacy attacks In location-sharing social networks
Proceedings of the 13th international conference on Ubiquitous computing
Proceedings of the Fifth Workshop on Social Network Systems
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Location information is becoming prevalent in today's online social networks (OSNs), which raises special privacy concerns with regard to both location sharing and its applications. Even when no explicit location is disclosed by a user, it is possible to geolocate the user through his/her social context, e.g., status updates and social relationships in OSNs. To demonstrate this, we propose GeoFind, which accurately identifies users' geographic regions through effective fusion (re-ranking) of (1) text-based ranking using geo-sensitive textual features and (2) structure-based ranking using maximum likelihood estimation (MLE) of geotagged friends. Evaluation results using 0.8 million geotagged Twitter users over a 3-month period demonstrate that GeoFind outperforms state-of-the-art techniques, with significant reduction of estimation error (25% of average error, 66% of median error). The potential of improving location accuracy through the fusion of multiple data types calls for a re-examination of existing privacy protection policies and mechanisms.