Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
A probabilistic approach to spatiotemporal theme pattern mining on weblogs
Proceedings of the 15th international conference on World Wide Web
World explorer: visualizing aggregate data from unstructured text in geo-referenced collections
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Towards automatic extraction of event and place semantics from flickr tags
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 15th international conference on Multimedia
World-scale mining of objects and events from community photo collections
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Placing flickr photos on a map
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
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
Exploration and comparison of geographic information sources using distance statistics
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Mining photo-sharing websites to study ecological phenomena
Proceedings of the 21st international conference on World Wide Web
Discovering geographical topics in the twitter stream
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
Hi-index | 0.00 |
It is nowadays possible to access a huge and increasing stream of social media records. Recently, such data has been used to infer about spatio-temporal phenomena by treating the records as proxy observations of the real world. However, since such observations are heavily uncertain and their spatio-temporal distribution is highly heterogeneous, extracting meaningful signals from such data is a challenging task. In this paper, we present a probabilistic model to extract spatio-temporal distributions of phenomena (called spatio-temporal signals) from social media. Our approach models spatio-temporal and semantic knowledge about real-world phenomena embedded in records on the basis of conditional probability distributions in a Bayesian network. Through this, we realize a generic and comprehensive model where knowledge and uncertainties about spatio-temporal phenomena can be described in a modular and extensible fashion. We show that existing models for the extraction of spatio-temporal phenomena distributions from social media are particular instances of our model. We quantitatively evaluate instances of our model by comparing the spatio-temporal distributions of extracted phenomena from a large Twitter data set to their real-world distributions. The results clearly show that our model allows to extract better spatio-temporal signals in terms of quality and robustness.