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 news stories submitted to Digg in June 2006. Our results suggest that pattern of the spread of interest in a story on the network is indicative of how popular the story will become. Stories that spread mainly outside of the submitter's neighborhood go on to be very popular, while stories that spread mainly through submitter's social neighborhood prove not to be very popular. This effect is visible already in the early stages of voting, and one can make a prediction about the potential audience of a story simply by analyzing where the initial votes come from.
Our findings are in line with conclusions of previous studies that showed that social networks play an important role in promoting and locating content @cite_18 @cite_0 @cite_15 . In particular, Lerman @cite_15 showed that users with larger social networks are more successful in getting their stories promoted to Digg's front page, even if the stories are not very interesting. These findings have implications for the design of social media and social networking sites. For example, some implementations of social recommendation may lead to the tyranny of the minority,'' where a small group of active, well-connected users dominate the site @cite_7 . Rather than being a liability, social networks can be used to, for example, more accurately assess the quality of content, as this paper shows.
@cite_0 @cite_18 @cite_7 @cite_15
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Online social networking sites like Orkut, YouTube, and Flickr are among the most popular sites on the Internet. Users of these sites form a social network, which provides a powerful means of sharing, organizing, and finding content and contacts. The popularity of these sites provides an opportunity to study the characteristics of online social network graphs at large scale. Understanding these graphs is important, both to improve current systems and to design new applications of online social networks. This paper presents a large-scale measurement study and analysis of the structure of multiple online social networks. We examine data gathered from four popular online social networks: Flickr, YouTube, LiveJournal, and Orkut. We crawled the publicly accessible user links on each site, obtaining a large portion of each social network's graph. Our data set contains over 11.3 million users and 328 million links. We believe that this is the first study to examine multiple online social networks at scale. Our results confirm the power-law, small-world, and scale-free properties of online social networks. We observe that the indegree of user nodes tends to match the outdegree; that the networks contain a densely connected core of high-degree nodes; and that this core links small groups of strongly clustered, low-degree nodes at the fringes of the network. Finally, we discuss the implications of these structural properties for the design of social network based systems. The social news aggregator Digg allows users to submit and moderate stories by voting on (digging) them. As is true of most social sites, user participation on Digg is non-uniformly distributed, with few users contributing a disproportionate fraction of content. We studied user participation on Digg, to see whether it is motivated by competition, fueled by user ranking, or social factors, such as community acceptance. For our study we collected activity data of the top users weekly over the course of a year. We computed the number of stories users submitted, dugg or commented on weekly. We report a spike in user activity in September 2006, followed by a gradual decline, which seems unaffected by the elimination of user ranking. The spike can be explained by a controversy that broke out at the beginning of September 2006. We believe that the lasting acrimony that this incident has created led to a decline of top user participation on Digg. Social media sites underscore the Web's transformation to a participatory medium in which users collaboratively create, evaluate, and distribute information. Innovations in social media have led to social information processing, a new paradigm for interacting with data. The social news aggregator Digg exploits social information processing for document recommendation and rating. Additionally, via mathematical modeling, it's possible to describe how collaborative document rating emerges from the independent decisions users make. Using such a model, the author reproduces observed ratings that actual stories on Digg have received.