Novelty and collective attention

By Fang Wu, Bernardo A. Huberman. Proceedings of the National Academy of Sciences of the United States of America, 2007

638 citations recorded by the source corpus.

The subject of collective attention is central to an information age where millions of people are inundated with daily messages. It is thus of interest to understand how attention to novel items propagates and eventually fades among large populations. We have analyzed the dynamics of collective attention among 1 million users of an interactive web site, digg.com, devoted to thousands of novel news stories. The observations can be described by a dynamical model characterized by a single novelty factor. Our measurements indicate that novelty within groups decays with a stretched-exponential law, suggesting the existence of a natural time scale over which attention fades.

Subfields: Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a8d9b84defac94f9f45f5d0c455ec4337fe09c58

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Citations in the corpus

Meme-tracking and the dynamics of the news cycle (2009, background): “One could take this decrease to be exponential in some polynomial function of t − tj based on research on novelty and attention [32]; or, following research on human response-time dynamics [24, 29], one could take it to be a heavy-tailed functional form.”
Trends in Social Media: Persistence and Decay (2011, background): “There is considerable evidence that one aspect that causes topics to decay over time is their novelty [11].”
Understanding online firestorms: Negative word-of-mouth dynamics in social media networks (2014, background): “Recent studies show that the halflife of Twitter memes and hashtags, the time after which 50% of the overall traffic is reached (Burton and Kebler 1960), is just on the order of hours or even minutes (Fang and Huberman 2007).”
Dynamical classes of collective attention in twitter (2011, background): “but also pose scientiﬁc challenges related to understanding the mechanisms ruling their dynamics [2, 7, 4, 14, 11].”
Modeling Information Diffusion in Implicit Networks (2010, background): “In order to account for this effect of recency and novelty [33] we introduce a multiplicative factor α(t) that models how much more/less influential a node is given the time at which it mentions the information.”
Competition among memes in a world with limited attention (2012, background): “Wu and Huberman 8 studied the decay in news popularity.”
Visual Analysis of Topic Competition on Social Media (2013, background): “Several studies have also shown that increasing the number of competing topics results in decreasing popularity of the topics [4, 48].”
Predicting the popularity of online content (2008, background): “It has been shown that the popularity distribution of Digg stories of a given age follows a lognormal distribution [22] that is the result of a growth mechanism with multiplicative noise, and can be described as”
Kernel Archetypal Analysis for Clustering Web Search Frequency Time Series (2014, background): “Understanding the dynamics of collective human attention has been called a key scientific challenge for the information age [11].”
Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks (2010, positive): “As the story ages, accumulation of new votes slows down (Wu and Huberman 2007) and ﬁnally saturates.”
Using early view patterns to predict the popularity of youtube videos (2013, methodology): “…Subject Descriptors C.4 [Computer Systems Organization]: Performance of Systems - Measurement techniques; H.3.5 [Information Storage and Retrieval]: Online Information Services - Web-based services General Terms Experimentation, Measurement Keywords popularity prediction; YouTube; regression models”
The Pulse of News in Social Media: Forecasting Popularity (2012, background): “Among these are models of viral marketing (Leskovec, Adamic, and Huberman 2007), models of attention on the web (Wu and Huberman 2007), cascading behavior in propagation of information (Gruhl et al. 2004) (Leskovec et al. 2007) and models that describe heavy tails in human dynamics (V ´ azquez et…”
The Anatomy of a Scientific Rumor (2013, methodology): “For this reason, we modify the model by introducing a variable activation rate λ, accounting for the decreasing interest on a tweet over time, according to recent studies suggesting the existence of a natural time scale over which attention fades [42].”