A web-based kernel function for measuring the similarity of short text snippets
Proceedings of the 15th international conference on World Wide Web
Tracking dragon-hunters with language models
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
What is Twitter, a social network or a news media?
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
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
Multimedia Search Without Visual Analysis: The Value of Linguistic and Contextual Information
IEEE Transactions on Circuits and Systems for Video Technology
ESA: emergency situation awareness via microbloggers
Proceedings of the 21st ACM international conference on Information and knowledge management
@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)
Who, where, when and what: discover spatio-temporal topics for twitter users
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Location extraction from disaster-related microblogs
Proceedings of the 22nd international conference on World Wide Web companion
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
Human sensing for smart cities
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Twitter is a widely-used social networking service which enables its users to post text-based messages, so-called tweets. POI tags on tweets can show more human-readable high-level information about a place rather than just a pair of coordinates. In this paper, we attempt to predict the POI tag of a tweet based on its textual content and time of posting. Potential applications include accurate positioning when GPS devices fail and disambiguating places located near each other. We consider this task as a ranking problem, i.e., we try to rank a set of candidate POIs according to a tweet by using language and time models. To tackle the sparsity of tweets tagged with POIs, we use web pages retrieved by search engines as an additional source of evidence. From our experiments, we find that users indeed leak some information about their accurate locations in their tweets.