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Social Networks and Social Information Filtering on Digg

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.


INTRODUCTION
The label "social media" has been attached to many Web sites -blogs, MySpace, Flickr, del.icio.us, Wikipedia -whose content is primarily user driven. The recent rise of social media sites underscores the transformation of the Web and how it is being used. Rather than searching for and passively consuming information found on Web pages, users are now actively creating, evaluating and distributing information. Newer scripting technologies and software tools allow anyone to seamlessly add content to a Web site -a new blog entry, or a change to an existing article, an image, a link, a vote or feedback comment -without being familiar with HTML or the underlying technologies used by that site. Most of the sites also include a social networking component, which enables users to build personal social networks by designating other users as "friends" or "contacts" in order to gain access to friends' activities. For example, Flickr [2] allows users to see in real time new images posted by friends. Another distinctive feature of the social media sites is their transparency. Every username, every descriptive tag is a hyperlink that can be used to navigate the site, and unless it has been designated private, all content is publicly viewable and in some cases, modifiable.
Many Web sites that provide information (or sell products or services) use collaborative filtering technology to suggest relevant documents (or products and services) to its users. Amazon and Netflix, for example, use collaborative filtering to recommend new books or movies to its users. Collaborative filtering-based recommendation systems [4] try to find users with similar interests by comparing their opinions about products. They will then suggest new products that were liked by other users with similar opinions. Recommender systems based on social filtering, on the other hand, suggest new products or documents simply based on whether the user's designated friends found these products or documents interesting. Researchers in the past have recognized that social networks present in the user base of the recommender system can be induced from the explicit and implicit declarations of user interest, and that these social networks can in turn be used to make new recommendations [7]. To the best of our knowledge, social media sites are the first systems to directly use social networks for social filtering.
In this paper we show that social filtering on Digg, a social news aggregator, is an effective recommendation system. Specifically, we show that Digg users tend to be interested in the news stories their friends find interesting. We also study the effect social filtering has on the organization of stories on Digg, including unintended consequences such as "tyranny of the minority." We compare Digg with Reddit, another social new aggregator that, unlike Digg, uses collaborative filtering to recommend news stories to its readers. Reddit's type of filtering appears to be much weaker, promoting stories that users do not find interesting. Although social filtering, as practiced by Digg, has recently come under fire, we believe it to be a promising technology that will lead to new generation of personalization and recommendation algorithms. Digg [1] is arguably one of the most successful social news aggregators. Its functionality is very simple. Users submit links to stories they find online, and other users vote on these stories. When a story gets enough positive votes, or diggs, it is promoted to the front page. The front page is what users see on the Digg home page, while the newly submitted stories are less visible, being "hidden" in the Upcoming stories pages.

STRUCTURE OF DIGG
A typical Digg page is shown in Figure 1. Each contains a list of 15 stories. The stories are in reverse chronological order of being submitted (for the upcoming stories queue) or promoted (for the front page stories), with most recent stories appearing at the top. The story's title is a link to the source, while clicking on the number of diggs takes one to the page describing the story's activity on digg: the discussion around it, the list of people who dugg it, etc. Digg also allows users to designate other users as friends. Digg makes it easy to track friends' activities. The left column on the home page summarizes the number of stories the friends have submitted, commented on or liked recently. It even has a handy feature to see the stories at least two friends have liked ("agreed on"). All these stories are also are flagged with a green ribbon (see fourth story in Figure 1) making them easy to spot. Tracking activities of friends is common feature in many social media sites and is one of the major draws attracting users to these sites. It offers a new paradigm for interacting with information -social filtering. Rather than actively searching for new interesting content, or subscribing to a set of predefined topics, users can now put other people to task of finding and filtering information for them.
Digg selects a handful of stories each day to feature on its front page. Getting to the front page is important to users, because it increases the story's visibility (most people who go to Digg only read the front page stories), as well as the visibility of the user who submitted the story. In fact, Digg ranks users based on how many of their stories made it to the front page, and improving one's rank has become a competitive sport. Although the exact formula for how a story is promoted to the front page is kept secret, so as to prevent users from "gaming the system" to promote bogus stories, it appears to take into account the number of diggs a story gets and the rate at which it gets them. The mechanism by which the stories are promoted, therefore, does not depend on the decision of one or few editors, but emerges from the activities of many users. We are interested in studying the mechanism by which such consensus emerges and the role social networks play in them.

DYNAMICS OF DIGGS
In order to see how consensus emerges from independent decisions made by many users, we tracked both new and front page stories in the technology category. We collected data by scraping Digg site with the help of Web wrappers, created with tools provided by Fetch Technologies: digg-frontpage wrapper extracts a list of stories from the first 14 pages of the home page. For each story, it extracts submitter's name, story title, time submitted, number of diggs and comments the story received.
digg-all wrapper extracts a list of stories from the first 20 pages in the Upcoming stories queue. For each story, it extracts the submitter's name, story title, time submitted, number of diggs and comments the story received.
digg-with-history wrapper extracts the same information as digg-frontpage wrapper, along with the list of the first 216 users who dugg the story.
top-users wrapper extracts information about the first 1020 recently active users. Since Digg ranks users by how many stories they have on the front page, we collect information about 1020 of the top ranked users. For each user, it extracts the number of stories that user has submitted, commented on, and dugg; number of stories that have been promoted to the front page; number of profile views; time account was established; users's rank; the list of friends (contacts), as well as reverse friends or "people who have befriended this user." Digg-frontpage and digg-all wrappers were executed hourly over a period of a week in May 2006. Top-users wrapper was executed at the same time to gather a snapshot of the social network of the top Digg users.
We identified stories that were submitted to Digg over the course of approximately one day and followed these stories over a period of six days. Of the 2858 stories that were submitted by 1570 users during this time period, only 98 stories by 60 different users made it to the front page. Figure 2(a) shows evolution of the ratings (number of diggs) of select stories. The basic dynamics of all the stories appears the same. A story accrues diggs at some rate. Once it is promoted to the front page, it accumulates diggs at a much faster rate. As the story ages, accumulation of new diggs slows down, and the story's rating saturates at some value. We will call the maximum diggs a story accrues its "interestingness", as it reflects how interesting the story is to the general audience.
It is worth noting that top rated users are not submitting stories that get the most diggs. This is shown graphically in Figure 2  Dashed lines correspond to stories submitted by users whose rank was greater than 1020, while solid lines correspond to stories submitted by users whose rank was less than 35. (b) Maximum number of diggs attained by a story during the period of observation vs submitter's rank. Symbols on the right axis correspond to low-rated users with rank> 1020.
stories came from 45 low-rated users. The mean "interestingness" of the stories submitted by the top-rated users is 600, almost half the average "interestingness" of the stories submitted by low-rated users. A second observation is that top-rated users are responsible for multiple front page stories. A look at the statistics about top users provided by Digg shows that this is generally the case: of the more than 15, 000 front page stories submitted by the top 1020 users, the top 3% of the users are responsible for 35% of the stories.

SOCIAL NETWORKS AND SOCIAL FIL-TERING
If top-ranked users do not submit the most interesting stories, why are they so successful? We believe that social filtering play a role in promoting stories to the front page. As we explained above, Digg's interface allows users to designate others as "friends" and easily keep track of friends' activities: the stories they have submitted, commented on or dugg. We believe that users use this feature to filter the tremendous number of new submissions on Digg. We show this by analyzing two sub-claims: (a) users digg stories their friends submit, and (b) users digg stories their friends digg.
Note that the "friend" relationship is not symmetric: if user A designates user B as a friend, user A can keep track of user B's activities, but not vice versa. This makes A the reverse friend of B. For the most part, users appear to take advantage of Digg's social networking feature, with the top users having bigger social networks. Users below the diagonal are watching more people than are watching them (fans), while users above the diagonal are being watched by more other users than they are watching (celebrities). Two of the biggest celebrities are users marked a and b on Figure 3(a). These users are kevinrose and diggnation, respectively, one of the founders of Digg and a podcast of the popular Digg stories.

Users digg stories their friends submit
In order to show that users digg stories their friends submit, we used digg-with-history wrapper to collect 195 front stories, each with a list of the first 216 users who dugg the story (15, 742 unique users total). The name of the submitter is first on the list.
We can compare the list of users who dugg the story, or any portion of this list, with the list of reverse friends of the submitter. Figure 3(b) shows the number of diggers of a story who are also among the reverse friends of the user who submitted the story, for all 195 stories. Dashed line shows the size of the social network (number of reverse friends) of the submitter. More than half of the stories (102) were submitted by users with one or more reverse friends, and the rest by unknown users. 1 Thin solid line shows how many people who list the submitter as a friend dugg the story within the first 215 diggs. All but two of the stories (submitted by SearchEngines with 21 reverse friends) were dugg by submitter's reverse friends. We use simple combinatorics [6] to compute the probability that k of the submitter's friends could have dugg the story purely by chance. The probability that after picking n = 215 users randomly from a pool of N = 15, 742 you end up with k that came from a group of size K is P (k, n) =`n k´( p) k (1 − p) n−k , where p = K/N . Using this formula, the probability (averaged over stories dugg by at least one friend) that the observed numbers of friends dugg the story by chance is P = 0.005, making it highly unlikely. 2 Moreover, users digg stories submitted by their friends very quickly. The heavy solid line in Figure 3(b) shows the number of reverse friends who were among the first 25 diggers. The probability that these numbers could have been observed by chance is even less -P = 0.003. We conclude that users digg stories their friends submit. A consequence of this conclusion is that users with active social networks are more successful in getting their stories

Users digg stories their friends digg
In the previous section we showed that by enabling users to quickly digg stories submitted by friends, social networks play an important role in promoting content to the front page. Do social networks also help users discover interesting stories that were submitted by unknown users (users who are not listed as friends by anyone)? Top users are very active. The top 3% of the 1020 recently active Digg users in our dataset is not only responsible for the disproportionate share of front page stories, but they also submit more than 28% of the stories submitted by the group of 1020 users, and digg 11% and comment on 8% of the stories dugg by and commented on by this group. Once one of these well connected users diggs a story, others within his or her social network will be more likely to read it thanks to the user interface of Digg that quickly allows a user to view stories dugg by friends. Do users digg stories dugg by friends? To answer this question we look at the 25 diggs that come after the first m diggs and see how many of them come from friends of the m diggers. Only ten of the stories were dugg by submitter's reverse friends. After five more users dugg the stories (m = 6), 75 became visible to others through the friends interface, and of these 23 were dugg by friends. After 25 users have dugg the story, all 96 stories were visible through the friends interface, and almost half of these were dugg by friends. Table 1 summarizes the observations and presents the probability that the observed numbers of friends dugg the story by chance. The probabilities for m = 26-m = 46 are above the 0.05 significance level, and possibly reflect the increased visibility the story receives once it makes it to the front page. Although the effect is not quite as dramatic as one in the previous section, we believe that the data shows that users do use the friends interface to find new interesting stories.

COMPARISON WITH REDDIT
Reddit [8] is another social news aggregator that allows users to submit and vote on stories. Stories that get enough positive votes are then promoted to the "hot" page, Reddit's version of the front page. Unlike Digg, Reddit does not have an explicit social networking component which allows a user to track friends's activities or browse another user's network of friends. 3 Instead, Reddit lets users discover new interesting stories through its recommendation system that uses collaborative filtering to suggest stories that were liked by other users with similar voting patterns. Alternately, a user can browse through the newly submitted stories.
Our dataset consists of statistics extracted from Reddit's new and hot pages over a period of two days in May, 2006. We identified 571 stories submitted by 350 users over the course of approximately a day. Of these, 260 stories by 192 users also appeared on the hot (front) page. Figure 5(a) shows how the number of points accumulated by stories on Reddit's hot page changes with time. Note that we were only able to track the stories for up to one day past submission time. At first glance, dynamics looks similar to Digg. Unlike Digg, however, a story often appears on the hot page at the same time it appears on the new page (squares in Figure 5). Also, unlike Digg, Reddit allows people to vote stories down. Figure 5(b) shows the maximum number of points achieved by Reddit stories over a period of about a day and the points these stories had at the end of the period. One can see that  Table 1: Number of stories posted by "unknown" users that were (a) made visible to other users through the digging activities of well-connected users, (b) dugg by friends of the first m diggers within the next 25 diggs, and for the stories that were dugg by friends, (c) the average probability that the observed numbers of friends could have dugg the story by chance the ratings of a substantial number of stories dropped, in many cases to zero, while other stories appeared on the front page with very few points and never went anywhere.
We were unable to obtain data to measure the effectiveness of Reddit's recommendation algorithm. We can only state that the algorithm Reddit uses to promote stories (which must consider actions of users reading and voting on recommended stories) appears to be less effective than Digg's in that it allows many more "uninteresting" stories (whose ratings do not increase) to the front page. This may account for the perception of Reddit as a timelier source of news. On Digg, a story has to accumulate enough votes before it is promoted, which takes time, while on Reddit, many stories appear to be promoted soon after posting, regardless of how many points they have accumulated. Although Reddit does not use the friends system, thus eliminating the possibility of "bloc voting," some users appear to be more successful than others in getting their stories promoted. In our dataset, there were an average of 1.4 front page stories per user on Reddit, compared to 1.6 on Digg.

TYRANNY OF THE MINORITY?
The new social media sites offer a glimpse into the future of the Web, where, rather than passively consuming information, users will actively participate in creating, evaluating, and disseminating information. Several such sites, Digg and Flickr, for example, allow users to designate select users as "friends" and provide easy interface to track friends' activities. Just as Google revolutionized Web search by exploiting the link structure of the Web -created independently through the activities of many Web page authors -to evaluate the contents of information on Web pages, social media sites show that it is possible to personalize search through social filtering that exploits the activities of others in the user's social network.
We studied the role social networks and social filtering play in the collaborative ranking of information. Specifically, we looked at how news stories submitted to Digg are promoted to its front page. Digg's goal is to have only the best of the stories featured on its front page, and it employs aggregated opinion of thousands of its users, rather than a few dedicated editors, to select the best stories. Digg also allows users to create social networks by designating others as friends and provides a seamless interface to track friends' activities: what stories users in their social network submitted, liked, commented on, etc. By tracking stories over time, we showed that social networks play an important role in collaborative information filtering. Specifically, we showed (a) users tend to like stories submitted by friends and (b) users tend to like stories their friends read and like. This, in a nutshell, is social filtering. Since some users are more active than others, direct implementation of social filtering may lead to "tyranny of the minority," where a lion's share of front page stories come from users with the most active social networks. This appears to be the case for Digg, where visualization of the graph of mutual friends shows a single cluster of composed of users among the 30 top-ranked individuals, giving them an edge in future success. However, precisely because these users are the most active ones, they play an important role in filtering information and bringing to other users's attention stories that would otherwise be buried in the onslaught of new submissions.
Recently, a similar finding [3] resulted in a controversy on Symbols on the right hand axis correspond to lowrated users with rank> 1020. Digg [5], in which users accused a "cabal" of top users of automatically digging each other's stories in order to promote them to the front page. The resulting uproar prompted Digg to change the algorithm it uses to promote stories. In order to discourage what was seen as "gaming" the system through "bloc voting," the new algorithm "will look at the unique digging diversity of the individuals digging the story" [9]. Preliminary results of the stories submitted in early November 2006 indicate that algorithm change did achieve the desired effect of reducing the top user dominance on the front page. Our analysis of the November data shows that of the 3015 stories submitted by 1866 users over about one day, 77 stories by 63 users were promoted to the front page. Figure 6 shows the maximum number of diggs received by these stories over a period of six days vs the rank of the submitting user. Compared to Figure 2, front page now has a greater diversity of users, with fewer users responsible for multiple front page stories. In fact, in our data set, there are 1.2 stories per submitting user, compared to 1.6 before. Although this may be seen as a positive development, the change in the story promotion algorithm may have some unintended consequences: it may, for example, discourage users from joining social networks because their votes will be discounted. It is too early to see what long term consequences, intended or not, the new algorithm will have.
Rather than being a liability, however, social networks can be used to personalize and tailor information to individual users, and drive the development of new social search algorithms. As Digg matures, we expect different sub-communities to arise, each representing users interested in a particular topic or a combination of topics. A single user could belong to several different communities, and use his or her social networks to find and filter interesting new information. For example, Digg can create personalized front pages for every user that are based on his or her friends' readings. This will finally free individuals from "tyranny of the majority" which results from viewing a common global front page or best seller list.
In order to be effective for personalizing information, the social networks created by users have to reflect their tastes and interests. Some users appear to accumulate contacts for the sake of having contacts, or reciprocate every request to be added to the contacts list. On Flickr, for example, we have observed some users with over 10,000 contacts. Publicly displaying one's tastes raises many privacy issues, which have yet to be addressed. Promising or perilous, social media appears to be the future of the Web.