Meme-tracking and the dynamics of the news cycle
KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, page 497--506. New York, NY, USA, ACM, (2009)DOI: 10.1145/1557019.1557077
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.
