Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamic aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given URL. We also present a dynamic model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.

In recent years, social media has become ubiquitous and important for social networking and content sharing. And yet, the content that is generated from these websites remains largely untapped. In this paper, we demonstrate how social media content can be used to predict real-world outcomes. In particular, we use the chatter from Twitter.com to forecast box-office revenues for movies. We show that a simple model built from the rate at which tweets are created about particular topics can outperform market-based predictors. We further demonstrate how sentiments extracted from Twitter can be further utilized to improve the forecasting power of social media.

An extensive analysis of user traffic on Gnutella shows a significant amount of free riding in the system. By sampling messages on the Gnutella network over a 24-hour period, we established that almost 70% of Gnutella users share no files, and nearly 50% of all responses are returned by the top 1% of sharing hosts. Furthermore, we found out that free riding is distributed evenly between domains, so that no one group contributes significantly more than others, and that peers that volunteer to share files are not necessarily those who have desirable ones. We argue that free riding leads to degradation of the system performance and adds vulnerability to the system. If this trend continues copyright issues might become moot compared to the possible collapse of such systems. Contents Introduction Gnutella Experiments Discussion Conclusions

We present a method for accurately predicting the long time popularity of online content from early measurements of user's access. Using two content sharing portals, Youtube and Digg, we show that by modeling the accrual of views and votes on content offered by these services we can predict the long-term dynamics of individual submissions from initial data. In the case of Digg, measuring access to given stories during the first two hours allows us to forecast their popularity 30 days ahead with remarkable accuracy, while downloads of Youtube videos need to be followed for 10 days to attain the same performance. The differing time scales of the predictions are shown to be due to differences in how content is consumed on the two portals: Digg stories quickly become outdated, while Youtube videos are still found long after they are initially submitted to the portal. We show that predictions are more accurate for submissions for which attention decays quickly, whereas predictions for evergreen content will be prone to larger errors.

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. economics of attention information access T he problem of collective attention is at the heart of decision making and the spread of ideas, and, as such, it has been studied at the individual and small group level by a number of psychologists (1, 2), economists, † and researchers in the area of marketing and advertising (3-5). Attention also affects the propagation of information in social networks, determining the effectiveness of advertising and viral marketing. ‡ And although progress on this problem has been made in small laboratory studies and in the theoretical literature of attention economics (6), it is still lacking empirical results from very large groups in a natural, nonlaboratory, setting. To understand the process underlying attention in large groups, consider as an example how a news story spreads among a group of people. When it first comes out, the story catches the attention of a few, who may further pass it on to others if they find it interesting enough. If a lot of people start to pay attention to this story, its exposure in the media will continue to increase. In other words, a positive-reinforcement effect sets in such that the more popular the story becomes, the faster it spreads. This growth is counterbalanced by the fact that the novelty of a story tends to fade with time and thus the attention that people pay to it. This can be due either to habituation or competition from other new stories, which is the regime recently studied by Falkinger (6). Therefore, in considering the dynamics of collective attention, two competing effects are present: the growth in the number of people that attend to a given story and the habituation or competition from other stories that makes the same story less likely to be attractive as time goes on. This process becomes more complex in the realistic case of multiple items or stories appearing at the same time, because now people also have the choice of which stories to focus on with their limited attention. To study the dynamics of collective attention and its relation to novel inputs in a natural setting, we analyzed the behavioral patterns of 1 million people interacting with a news web site whose content is solely determined by its own users. Because people using this web site assign each news story an explicit measure of popularity, we were able to determine the growth and decay of attent...