Patterns of temporal variation in online media

By Jaewon Yang, Jure Leskovec. Web Search and Data Mining, 2011

1,092 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Economics and Econometrics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a4229cd9b29c1623339b37f515ae858b6ac873b7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Economics and Econometrics

In Signal Processing

Citations in the corpus

Effects of Disaster Characteristics on Twitter Event Signature (2014, background): “While outside the scope of crisis informatics, the work most closely related to our notion of event signatures considered the more general phenomenon of temporal variation of content on Twitter [11].”
Trends in Social Media: Persistence and Decay (2011, background): “Yang and Leskovec [12] examined patterns of temporal behavior for hashtags in Twitter.”
The Simple Rules of Social Contagion (2013, neutral): “Although diﬀerent types of information may spread according to slightly diﬀer-ent patterns [35, 36] our analysis is content agnostic, so the reported results are the population average, with the caveat that we have removed most spam.”
Understanding Short-term Changes in Online Activity Sessions (2017, background): “Leskovec et al. developed a framework for tracking variants of short textual phrases over time [15] and found prototypical temporal patterns in the spread of news stories [27].”
Sensing Trending Topics in Twitter (2013, positive): “The idea behind those methods is that breaking news, unlike other discussion topics, happen to reach a fast peak of attention from social media users as soon as they are publicly announced [26],”
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity (2015, methodology): “These methods have been successfully applied to study the spread of memes on the web [10, 14, 32, 33] Twitter [36].”
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.”
Social Clicks: What and Who Gets Read on Twitter? (2016, neutral): “, tweets [31], URLs shorteners, diggs [28]) ignoring which part of those content exposure leads to actual clicks and content being read.”
Understanding the Factors That Impact the Popularity of GitHub Repositories (2016, methodology): “To this purpose, we use the KSC algorithm [11].”
What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities (2012, methodology): “An algorithm for clustering and predicting patterns of short temporal variations was proposed by [34, 35].”