NewsStand: A New View on News. Teitler, B. E.; Lieberman, M. D.; Panozzo, D.; Sankaranarayanan, J.; Samet, H.; and Sperling, J. In
doi  abstract   bibtex   
News articles contain a wealth of implicit geographic content that if exposed to readers improves understanding of today's news. However, most articles are not explicitly geotagged with their geographic content, and few news aggregation systems expose this content to users. A new system named NewsStand is presented that collects, analyzes, and displays news stories in a map interface, thus leveraging on their implicit geographic content. NewsStand monitors RSS feeds from thousands of online news sources and retrieves articles within minutes of publication. It then extracts geographic content from articles using a custom-built geotagger, and groups articles into story clusters using a fast online clustering algorithm. By panning and zooming in NewsStand's map interface, users can retrieve stories based on both topical significance and geographic region, and see substantially different stories depending on position and zoom level.
@inproceedings{ tei08,
  crossref = {acmgis2008},
  author = {Benjamin E. Teitler and Michael D. Lieberman and Daniele Panozzo and Jagan Sankaranarayanan and Hanan Samet and Jon Sperling},
  title = {NewsStand: A New View on News},
  doi = {10.1145/1463434.1463458},
  uri = {http://www.cs.umd.edu/~hjs/pubs/newsstand-acmgis2008.pdf},
  abstract = {News articles contain a wealth of implicit geographic content that if exposed to readers improves understanding of today's news. However, most articles are not explicitly geotagged with their geographic content, and few news aggregation systems expose this content to users. A new system named NewsStand is presented that collects, analyzes, and displays news stories in a map interface, thus leveraging on their implicit geographic content. NewsStand monitors RSS feeds from thousands of online news sources and retrieves articles within minutes of publication. It then extracts geographic content from articles using a custom-built geotagger, and groups articles into story clusters using a fast online clustering algorithm. By panning and zooming in NewsStand's map interface, users can retrieve stories based on both topical significance and geographic region, and see substantially different stories depending on position and zoom level.}
}
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