Identifying, attributing and describing spatial bursts
Proceedings of the VLDB Endowment
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
Efficient continuously moving top-k spatial keyword query processing
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Identification of live news events using Twitter
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Parallel main-memory indexing for moving-object query and update workloads
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Open domain event extraction from twitter
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Real-time spatio-temporal analysis of West Nile virus using Twitter data
Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications
Socioscope: spatio-temporal signal recovery from social media
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
A framework for efficient spatial web object retrieval
The VLDB Journal — The International Journal on Very Large Data Bases
ER'12 Proceedings of the 31st international conference on Conceptual Modeling
Microblog-genre noise and impact on semantic annotation accuracy
Proceedings of the 24th ACM Conference on Hypertext and Social Media
Semantic Characterization of Tweets Using Topic Models: A Use Case in the Entertainment Domain
International Journal on Semantic Web & Information Systems
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Social media has changed the way we communicate. Social media data capture our social interactions and utterances in machine readable format. Searching and analysing massive and frequently updated social media data brings significant and diverse rewards across many different application domains, from politics and business to social science and epidemiology. A notable proportion of social media data comes with explicit or implicit spatial annotations, and almost all social media data has temporal metadata. We view social media data as a constant stream of data points, each containing text with spatial and temporal contexts. We identify challenges relevant to each context, which we intend to subject to context aware querying and analysis, specifically including longitudinal analyses on social media archives, spatial keyword search, local intent search, and spatio-temporal intent search. Finally, for each context, emerging applications and further avenues for investigation are discussed.