Longitudinal study of changes in blogs
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
ARSA: a sentiment-aware model for predicting sales performance using blogs
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Application of kalman filters to identify unexpected change in blogs
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
On the spatiotemporal burstiness of terms
Proceedings of the VLDB Endowment
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Previous work on spatio-temporal analysis of news items and other documents has largely focused on broad categorization of small text collections by region or country. A system for large-scale spatio-temporal analysis of online news media and blogs is presented, together with an analysis of global news media coverage over a nine year period. We demonstrate the benefits of using a hierarchical geospatial database to disambiguate between geographical named entities, and provide results for an extremely fine-grained analysis of news items. Aggregate maps of media attention for particular places around the world are compared with geographical and socio-economic data. Our analysis suggests that GDP per capita is the best indicator for media attention.