Methods for extracting place semantics from Flickr tags
ACM Transactions on the Web (TWEB)
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
The wisdom of social multimedia: using flickr for prediction and forecast
Proceedings of the international conference on Multimedia
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
Proceedings of the fifth ACM international conference on Web search and data mining
Mining photo-sharing websites to study ecological phenomena
Proceedings of the 21st international conference on World Wide Web
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
Latent geographic feature extraction from social media
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Modeling locations with social media
Information Retrieval
Methods for extracting place semantics from Flickr tags
ACM Transactions on the Web (TWEB)
Spatio-temporal characteristics of bursty words in Twitter streams
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Shared multimedia, microblogs, search engine queries, user comments, and location check-ins, among others, generate an enormous stream of human activity records. Such records consist of information in the form of text, images, or videos, and can often be traced in time and space using associated time/location information. Over the past years such spatio-temporal activity streams have been heavily studied with the aim to extract and explore spatio-temporal phenomena, like events, place descriptions, and geographical topics. Despite the clear intuition and often simple techniques to extract such knowledge, the amount of noise, sparsity, and heterogeneity in the data makes such tasks non-trivial and erroneous. This demonstration offers a visual interface to compare, combine, and evaluate spatio-temporal signal extraction and exploration approaches from large-scale sets of human activity records.