Social Networks and Social Information Filtering on Digg

TL;DR

This paper investigates how social networks on Digg influence the promotion of news stories, demonstrating that social filtering effectively predicts user preferences and impacts story visibility.

Contribution

It provides empirical evidence that social filtering based on user networks is effective and explores its implications for personalized information delivery and potential biases.

Findings

Social filtering predicts user preferences effectively.

Users tend to like stories from friends and read stories liked by friends.

Social networks influence story promotion to the front page.

Abstract

The new social media sites -- blogs, wikis, Flickr and Digg, among others -- underscore the transformation of the Web to a participatory medium in which users are actively creating, evaluating and distributing information. Digg is a social news aggregator which allows users to submit links to, vote on and discuss news stories. Each day Digg selects a handful of stories to feature on its front page. Rather than rely on the opinion of a few editors, Digg aggregates opinions of thousands of its users to decide which stories to promote to the front page. Digg users can designate other users as ``friends'' and easily track friends' activities: what new stories they submitted, commented on or read. The friends interface acts as a \emph{social filtering} system, recommending to user stories his or her friends liked or found interesting. By tracking the votes received by newly submitted stories over time, we showed that social filtering is an effective information filtering approach. Specifically, we showed that (a) users tend to like stories submitted by friends and (b) users tend to like stories their friends read and liked. As a byproduct of social filtering, social networks also play a role in promoting stories to Digg's front page, potentially leading to ``tyranny of the minority'' situation where a disproportionate number of front page stories comes from the same small group of interconnected users. Despite this, social filtering is a promising new technology that can be used to personalize and tailor information to individual users: for example, through personal front pages.