Meme-tracking and the dynamics of the news cycle

By Jure Leskovec, Lars Backstrom, Jon M. Kleinberg. 2009

1,678 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics, Social Sciences & Humanities

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Citations in the corpus

Determinantal Point Processes for Machine Learning (2012, background): “Other work in the topic tracking literature has addressed related tasks [11, 91, 105].”
Scalable Influence Estimation in Continuous-Time Diffusion Networks (2013, methodology): “We use the MemeTracker dataset [23] which has 10,967 hyperlink cascades among 600 media sites.”
Inferring networks of diffusion and influence (2010, methodology): “For this analysis, we use theMemeTracker phrase clusters as cascades and analyze the structure of the inferred information diffusion network.”
LCSTS: A Large Scale Chinese Short Text Summarization Dataset (2015, background): “6 million mainstream media sites and blogs and mine a set of novel and persistent temporal patterns in the news cycle (Leskovec et al., 2009).”
Online Visual Analytics of Text Streams (2015, background): “MemeTracker [23] was developed to effectively identify phrase-based topics from millions of news articles.”
Quantifying Information Overload in Social Media and Its Impact on Social Contagions (2014, background): “…a large number of previous studies have focused on social contagions in on-line media (Du et al. 2013; Goel, Watts, and Goldstein 2012; Gomez-Rodriguez, Leskovec, and Krause 2010; Leskovec, Backstrom, and Kleinberg 2009), most ignore the impact of information overload on users’ forwarding behavior.”
A temporal model of text periodicities using Gaussian Processes (2013, background): “Temporal patterns for short, distinctive lexical items such as hashtags and memes were quantitatively studied (Leskovec et al., 2009) and clustered (Yang and Leskovec, 2011) in Social Media.”
Comparing Events Coverage in Online News and Social Media: The Case of Climate Change (2015, background): “Similar observations hold for news consumption (Leskovec, Backstrom, and Kleinberg 2009; Castillo et al. 2014).”
Uncover Topic-Sensitive Information Diffusion Networks (2013, methodology): “In both synthetic and real-world data, we show that our method significantly improves over the previous state-of-the-art models in terms of both recovering the hidden diffusion networks and predicting the transmission times of memes.”
A Frame of Mind: Using Statistical Models for Detection of Framing and Agenda Setting Campaigns (2015, result): “Finally, in practice it is usually sufficient restrict the autoregressive term to one parameter with j = 1 (accounting to the y value at the previous time stamp), this is consistent with the 24 hours news cycle reported by (Leskovec et al., 2009) among others.”