Social dynamics of Digg

By Tad Hogg, Kristina Lerman. 2010

166 citations recorded by the source corpus.

Online social media provide multiple ways to find interesting content. One important method is highlighting content recommended by user’s friends. We examine this process on one such site, the news aggregator Digg. With a stochastic model of user behavior, we distinguish the effects of the content visibility and interestingness to users. We find a wide range of interest and distinguish stories primarily of interest to a users’ friends from those of interest to the entire user community. We show how this model predicts a story’s eventual popularity from users’ early reactions to it, and estimate the prediction reliability. This modeling framework can help evaluate alternative design choices for displaying content on the site.

Subfields: Sociology and Political Science, 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 Sociology and Political Science

In Statistical and Nonlinear Physics

Citations in the corpus

Computational social scientist beware: Simpson’s paradox in behavioral data (2017, background): “2 in [11]) only become apparent once the activity of tens of thousands or even millions of people is aggregated.”
Leveraging Position Bias to Improve Peer Recommendation (2014, background): “For example, the default presentation of news stories shown in Digg’s front page (circa 2009) was by the time of promotion, which corresponds to a fixed ordering, since every user sees the stories in the same order.”
DeepInf: Social Influence Prediction with Deep Learning (2018, background): “Digg [23] Digg is a news aggregator which allows people to vote web content, a.k.a, story, up or down.”
Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks (2010, unknown): “Separating in-network from out-of-network activity allows us, among other things, to better estimate the inherent quality of the contributions (Crane and Sornette 2008) or predict their future activity (Hogg and Lerman 2010; Lerman and Galstyan 2008).”
Disentangling the Effects of Social Signals (2014, background): “s, indicating a ﬁve-fold variation in story appeal. This conﬁrms that our experiment design qualitatively corresponds to the large variation in preferences for content seen on social media web sites (Hogg and Lerman, 2012) and other domains, e.g., scientiﬁc papers (Wang et al., 2013). Fig. 2(b) show”
The "Majority Illusion" in Social Networks (2015, unknown): “We looked at three typical social and communications networks: the co-authorship network of high energy physicists (HepTh) [24], social media follower graph (Digg) [20], and the network representing links between political blogs (blogs) [2].”
Information is not a Virus, and Other Consequences of Human Cognitive Limits (2016, background): “When a newly submitted story accumulated enough votes, it was promoted to Digg’s front page, where it was visible to everyone, not only to followers of voters [22].”
Topological Recurrent Neural Network for Diffusion Prediction (2017, background): “• Digg [37] contains diffusions of stories as voted by the users, along with friendship network of the users.”