document: 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 but have never been captured accurately enough to permit actual quantitative analysis . We tracked 1.6 million mainstream media sites and blogs over a period of three months with the total of 90 million articles and we find a set of novel and persistent temporal patterns in the news cycle . In particular , we observe a typical lag of 2.5 hours between the peaks of attention to a phrase in the news media and in blogs respectively , with divergent behavior around the overall peak and a `` heartbeat '' - like pattern in the handoff between news and blogs . We also develop and analyze a mathematical model for the kinds of temporal variation that the system exhibits .
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