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.
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. Online peer production systems have enabled people to coactively create, share, classify, and rate content on an unprecedented scale. This paper describes strong macroscopic regularities in how people contribute to peer production systems, and shows how these regularities arise from simple dynamical rules. First, it is demonstrated that the probability a person stops contributing varies inversely with the number of contributions he has made. This rule leads to a power law distribution for the number of contributions per person in which a small number of very active users make most of the contributions. The rule also implies that the power law exponent is proportional to the effort required to contribute, as justified by the data. Second, the level of activity per topic is shown to follow a lognormal distribution generated by a stochastic reinforcement mechanism. A small number of very popular topics thus accumulate the vast majority of contributions. These trends are demonstrated to hold across hundreds of millions of contributions to four disparate peer production systems of differing scope, interface style, and purpose. 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. We describe a general stochastic processes-based approach to modeling user-contributory web sites, where users create, rate and share content. These models describe aggregate measures of activity and how they arise from simple models of individual users. This approach provides a tractable method to understand user activity on the web site and how this activity depends on web site design choices, especially the choice of what information about other users’ behaviors is shown to each user. We illustrate this modeling approach in the context of user-created content on the news rating site Digg. With the rise of web 2.0 there is an ever-expanding source of interesting media because of the proliferation of usergenerated content. However, mixed in with this is a large amount of noise that creates a proverbial “needle in the haystack” when searching for relevant content. Although there is hope that the rich network of interwoven metadata may contain enough structure to eventually help sift through this noise, currently many sites serve up only the “most popular” things. Identifying only the most popular items can be useful, but doing so fails to take into account the famous “long tail” behavior of the web—the notion that the collective effect of small, niche interests can outweigh the market share of the few blockbuster (i.e. most-popular) items—thus providing only content that has mass appeal and masking the interests of the idiosyncratic many. YouTube, for example, hosts over 40 million videos— enough content to keep one occupied for more than 200 years. Are there intelligent tools to search through this information-rich environment and identify interesting and relevant content? Is there a way to identify emerging trends or “hot topics” in addition to indexing the long tail for content that has real value?