Meme-tracking and the dynamics of the news cycle

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.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.

Uncovering the structure and temporal dynamics of information propagation

“…However, it happens more frequently that the largest amount of links are mainstream media to mainstream media, and the fewest links point from blogs to mainstream media. These results are intuitive and consistent with previous works (Gomez-Rodriguez et al, 2010;Leskovec et al, 2009) that observed that most often information flows from mainstream media to blogs (and rarely the other way around). However, as we see here for population-level events and social movements (such as in case of the civil unrest in the Middle East), social media plays a crucial role in information dissemination and organization of civil movements.…”

“…We trace the flow of information using short textual phrases (such as, "lipstick on a pig") that travel through the Web, which act as tracers for memes (Leskovec et al, 2009 Since all documents that contain memes are time-stamped, a cascade c m is simply a record of the times when sites first mentioned meme m. We extracted more than 179 million memes, longer than four words. Out of these, 34 million distinct memes appeared at least twice, resulting in 34 million meme cascades.…”

Peddling or Creating? Investigating the Role of Twitter in News Reporting

“…[14]. We take a different approach: we start with news story already "discovered" and investigate whether social media provides a different reporting to traditional media.…”

Online Social Networks Flu Trend Tracker: A Novel Sensory Approach to Predict Flu Trends

“…Sakaki et al used a probabilistic spatio-temporal model to build an autonomous earthquake reporting system in Japan using twitter users as sensors and applying Kalman filtering and particle filtering for location estimation [19]. Meme Tracking in news cycles as explained by Leskovec et al was an attempt to model information diffusion in social media like blogs and tracking handoff from professional news media to social networks [13].…”