Evaluation of an inference network-based retrieval model
ACM Transactions on Information Systems (TOIS) - Special issue on research and development in information retrieval
GIPSY: automated geographic indexing of text documents
Journal of the American Society for Information Science - Special issue: spatial information
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Web-a-where: geotagging web content
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A Graph-Ranking Algorithm for Geo-Referencing Documents
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Placing flickr photos on a map
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
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
An efficient location extraction algorithm by leveraging web contextual information
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
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
Location relevance classification for travelogue digests
Proceedings of the 20th international conference companion on World wide web
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
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On-line Social Networks have increased their popularity rapidly since their creation, providing a huge amount of data which can be leverage to extract useful information related to commercial and social human behaviours. One of the most useful information that can be extracted is the geographical one. This paper shows an approach to detect the geographical focus of Twitter users at city level based on the text of the tweets that users have sent and external information from Wikipedia. The main goal of this work is to show how important could be external formal text resources such as Wikipedia when it comes to resolve the geographical focus in short pieces of informal natural language text. In order to accomplish this objective, we have assessed our system with a language model system, comparing the results using only the informal pieces of text (tweets) and merging it with formal text coming from Wikipedia. In our experiments, we found that the aid of formal pieces of text, such as those obtained from the Wikipedia articles and links, could be useful when the existing amount of data is rather limited.