Analysis of Social Voting Patterns on Digg
Kristina Lerman, Aram Galstyan

TL;DR
This paper analyzes how social voting patterns on Digg influence the popularity of news stories, revealing that early spread outside the submitter's network predicts higher popularity.
Contribution
It introduces a method to predict story popularity based on early voting patterns and social network analysis on Digg.
Findings
Stories spreading outside the submitter's network tend to become more popular.
Early voting patterns can predict the eventual popularity of stories.
Social network structure influences content dissemination and success.
Abstract
The social Web is transforming the way information is created and distributed. Blog authoring tools enable users to publish content, while sites such as Digg and Del.icio.us are used to distribute content to a wider audience. With content fast becoming a commodity, interest in using social networks to promote and find content has grown, both on the side of content producers (viral marketing) and consumers (recommendation). Here we study the role of social networks in promoting content on Digg, a social news aggregator that allows users to submit links to and vote on news stories. Digg's goal is to feature the most interesting stories on its front page, and it aggregates opinions of its many users to identify them. Like other social networking sites, Digg allows users to designate other users as ``friends'' and see what stories they found interesting. We studied the spread of interest in…
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Taxonomy
TopicsComplex Network Analysis Techniques · Opinion Dynamics and Social Influence · Social Media and Politics
