Meme-tracking and the dynamics of the news cycle

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. We develop a framework for tracking short, distinctive phrases that travel relatively intact through on-line text; developing scalable algorithms for clustering textual variants of such phrases, we identify a broad class of memes that exhibit wide spread and rich variation on a daily basis. As our principal domain of study, we show how such a meme-tracking approach can provide a coherent representation of the news cycle -- the daily rhythms in the news media that have long been the subject of qualitative interpretation ...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Jure Leskovec, Lars Backstrom, Jon M. Kleinberg
• Keywords: Data Mining, Distinctive Phrases, KDD 2009, Long Time Scales, Particular Named Entities