Meme-tracking and the dynamics of the news cycle Export

@inproceedings{citeulike:5043997, abstract = {Tracking new topics, ideas, and "memes" across the Web has been an issue of considerable interest. Recent work has developed methods for tracking topic shifts over long time scales, as well as abrupt spikes in the appearance of particular named entities. However, these approaches are less well suited to the identification of content that spreads widely and then fades over time scales on the order of days - the time scale at which we perceive news and events.}, address = {New York, NY, USA}, author = {Leskovec, Jure and Backstrom, Lars and Kleinberg, Jon}, booktitle = {KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining}, citeulike-article-id = {5043997}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1557019.1557077}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1557019.1557077}, comment = {Presents an algorithm for tracking phrases (with near-duplicates) spreading across documents.}, doi = {10.1145/1557019.1557077}, isbn = {978-1-60558-495-9}, keywords = {news, text, time}, location = {Paris, France}, pages = {497--506}, posted-at = {2009-07-03 09:26:50}, priority = {0}, publisher = {ACM}, title = {Meme-tracking and the dynamics of the news cycle}, url = {http://dx.doi.org/10.1145/1557019.1557077}, year = {2009} }

TY - CONF ID - citeulike:5043997 L3 - citeulike-article-id:5043997 N2 - Tracking new topics, ideas, and "memes" across the Web has been an issue of considerable interest. Recent work has developed methods for tracking topic shifts over long time scales, as well as abrupt spikes in the appearance of particular named entities. However, these approaches are less well suited to the identification of content that spreads widely and then fades over time scales on the order of days - the time scale at which we perceive news and events. CY - Paris, France SN - 978-1-60558-495-9 AD - New York, NY, USA EP - 506 TI - Meme-tracking and the dynamics of the news cycle PB - ACM T2 - KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining SP - 497 KW - news KW - text KW - time N1 - Presents an algorithm for tracking phrases (with near-duplicates) spreading across documents. AU - Leskovec, Jure AU - Backstrom, Lars AU - Kleinberg, Jon PY - 2009/// UR - http://dx.doi.org/10.1145/1557019.1557077 ER -