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
Chatter on the red: what hazards threat reveals about the social life of microblogged information
Proceedings of the 2010 ACM conference on Computer supported cooperative work
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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
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
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
"I'm eating a sandwich in Glasgow": modeling locations with tweets
Proceedings of the 3rd international workshop on Search and mining user-generated contents
A sensitive Twitter earthquake detector
Proceedings of the 22nd international conference on World Wide Web companion
Location-based insights from the social web
Proceedings of the 22nd international conference on World Wide Web companion
Location extraction from disaster-related microblogs
Proceedings of the 22nd international conference on World Wide Web companion
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In order to sense and analyze disaster information from social media, microblogs as sources of social data have recently attracted attention. In this paper, we attempt to discover geolocation information from microblog messages to assess disasters. Since microblog services are more timely compared to other social media, understanding the geolocation information of each microblog message is useful for quickly responding to a sudden disasters. Some microblog services provide a function for adding geolocation information to messages from mobile device equipped with GPS detectors. However, few users use this function, so most messages do not have geolocation information. Therefore, we attempt to discover the location where a message was generated by using its textual content. The proposed method learns associations between a location and its relevant keywords from past messages, and guesses where a new message came from.