Robust dynamic classes revealed by measuring the response function of a social system

By Riley Crane, Didier Sornette. 2008

837 citations recorded by the source corpus.

We study the relaxation response of a social system after endogenous and exogenous bursts of activity using the time series of daily views for nearly 5 million videos on YouTube. We find that most activity can be described accurately as a Poisson process. However, we also find hundreds of thousands of examples in which a burst of activity is followed by an ubiquitous power-law relaxation governing the timing of views. We find that these relaxation exponents cluster into three distinct classes and allow for the classification of collective human dynamics. This is consistent with an epidemic model on a social network containing two ingredients: a power-law distribution of waiting times between cause and action and an epidemic cascade of actions becoming the cause of future actions. This model is a conceptual extension of the fluctuation-dissipation theorem to social systems [Ruelle, D (2004) Phys Today 57:48–53] and [Roehner BM, et al., (2004) Int J Mod Phys C 15:809–834], and provides a unique framework for the investigation of timing in complex systems.

Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

How researchers see this paper

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

In Experimental and Cognitive Psychology

In Statistical and Nonlinear Physics

Citations in the corpus

Inferring networks of diffusion and influence (2010, background): “In the end, our algorithm does not depend on the particular ch oice of the waiting time distribution and more complicated funct ions can easily be chosen [6].”
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity (2015, background): “• Dynamic Poisson Model (DPM) [2, 7]: It models the retweet times {tk} as a point process with rate λt = λtpeak (t− tpeak) γ”
Social Networks and the Diffusion of User-Generated Content: Evidence from YouTube (2012, background): “Information Systems Research, Articles in Advance, pp. 1–19, © 2011 INFORMS languish in obscurity (e.g., Crane and Sornette 2008).”
Prediction (2010, neutral): “The analogy of stress would be news but we are only starting to understand what is a “news” size and how to quantify it via the response function of socia l networks (see [104, 23, 135, 18]).”
Collective Response of Human Populations to Large-Scale Emergencies (2011, neutral): “Despite the clear temporal and spatial localization of anomalous call activity during emergencies, one expects some degree of information propagation beyond the eyewitness population [26].”
Patterns of temporal variation in online media (2011, background): “Patterns of human attention [34, 35], popularity [24, 30] and response dynamics [6, 10] have been extensively studied.”
Hip and trendy: Characterizing emerging trends on Twitter (2011, methodology): “The authors based their analysis on the ﬁndings of Crane and Sornette (2008), which analyzed time series viewing data for individual YouTube videos.”
Competition among memes in a world with limited attention (2012, background): “Crane and Sornette 10 introduced a model to describe the exogenous and endogenous bursts of attention toward a video, by combining an epidemic spreading process with a forgetting mechanism.”
Clustering daily patterns of human activities in the city (2012, methodology): “Emerging urban sensing data such as massive mobile phone data, and online user-generated social media data, both in the physical and virtual world (Crane and Sornette 2008; Kim et al. 2006), has been accompanied by the development of data mining and statistical learning techniques (Kargupta and Han ”
Social Clicks: What and Who Gets Read on Twitter? (2016, neutral): “However, this study considered clicks originating from any sources, including YouTube own recommendation systems, search engine and other mentions online.”
Mixture of Mutually Exciting Processes for Viral Diffusion (2013, methodology): “It was recently applied to modeling viral marketing (Crane & Sornette, 2008) and finance trades (Bacrya et al., 2012).”
Portfolio Choice in Markets with Contagion (2012, positive): “ction times and price changes at high frequency (Bowsher (2007)), trading activity at diﬀerent maturities of the yield curve (Salmon and Tham (2007)) and propagation phenomena in social interactions (Crane and Sornette (2008).) We extend the pure jump Hawkes model employed in the above applications,”
Science communication on YouTube: Factors that affect channel and video popularity (2016, background): “Figueiredo et al. (2011) similarly found that top videos (the quality category in Crane and Sornette (2008)) experience a significant burst of activity, receiving many views in a single day or week, with other videos undergoing several smaller peaks of activity.”
Rise and fall patterns of information diffusion: model and implications (2012, methodology): “Do real rise-and-fall patterns have any qualitative differences? Do they form different classes? If yes, how many? Earlier work on Youtube data claims there are four classes [6].”
Who says what to whom on twitter (2011, background): “In this sense, it is related to recent work by Crane and Sornette [5], who posited a mathematical model of social influence to account for observed temporal patterns in the popularity of YouTube videos, and also to Gomez et al [7], who studied the diffusion of information among blogs and online news”
Using early view patterns to predict the popularity of youtube videos (2013, methodology): “Indeed, Crane and Sornette have distinguished four different popularity evolution patterns among YouTube videos [5], which are explained in terms of a combination of endogenous (i.”