http://www.hpl.hp.com/research/scl/papers/bundles/bundles.pdf Clusters in social networks can help design customized bundles of products for consumers. Identifying consumer preferences is a key challenge in customizing electronic commerce sites to individual users. The increasing availability of online social networks provides one approach to this problem: people linked in these networks often share preferences, allowing inference of interest in products based on knowledge of a consumer's network neighbors and their interests. This paper evaluates the benefits of inference from online social networks in two contexts: a random graph model and a web site allowing people to both express preferences and form distinct social and preference links. We determine conditions on network topology and preference correlations leading to extended clusters of people with similar interests. Knowledge of when such clusters occur improves the usefulness of social network-based inference for identifying products likely to interest consumers based on information from a few people in the network. Such estimates could help sellers design customized bundles of products and improve combinatorial auctions for complementary products. To appear in Electronic Commerce Research and Applications special issue on Social Networks. Tad Hogg 2009-04-27 21:09:00 social networks electronic commerce essembly EC http://arxiv.org/abs/0904.0016 Fans, the law of web surfing and users' interests combine to promote and rate stories on Digg. 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. (In Proc. of the 3rd Int'l AAAI Conference on Weblogs and Social Media) Tad Hogg and Kristina Lerman 2009-04-02 00:39:00 ICWSM digg social media social networks online content popularity http://www.hpl.hp.com/research/scl/papers/twitter the social network that matters is not the one you declare. attention twitter social networks social media First Monday Scholars, advertisers and political activists see massive online social networks as a representation of social interactions that can be used to study the propagation of ideas, social bond dynamics and viral marketing, among others. But the linked structures of social networks do not reveal actual interactions among people. Scarcity of attention and the daily rhythms of life and work makes people default to interacting with those few that matter and that reciprocate their attention. A study of social interactions within Twitter reveals that the driver of usage is a sparse and hidden network of connections underlying the declared set of friends and followers. Bernardo A. Huberman, Daniel M. Romero and Fang Wu 2008-12-05 15:27:00 http://www.hpl.hp.com/research/scl/papers/sncf/ User-generated social networking links can be as predictive as algorithmically identified "neighbors" in recommender systems. collaborative filtering social networks prediction recommender systems essembly This paper demonstrates that "social network collaborative filtering" (SNCF), wherein user-selected like-minded alters are used to make predictions, can rival traditional user-to-user collaborative filtering (CF) in predictive accuracy. Using a unique data set from an online community where users rated items and also created social networking links specifically intended to represent like-minded allies, we use SNCF and traditional CF to predict ratings by networked users. We find that SNCF using generic "friend" alters is moderately worse than the better CF techniques, but outperforms benchmarks such as by-item or by-user average rating; generic friends often are not like-minded. However, SNCF using "ally" alters is competitive with CF. These results are significant because SNCF is tremendously more computationally efficient than traditional user-user CF and may be implemented in large-scale web commerce and social networking communities. It is notoriously difficult to distinguish the contributions of social influence (where allies influence users) and social selection (where users are simply effective at selecting like-minded people as their allies). Nonetheless, comparing similarity over time, we do show no evidence of strong social influence among allies or friends. Rong Zheng, Dennis M. Wilkinson and Foster Provost 2008-10-06 12:00:00 http://www.hpl.hp.com/research/scl/papers/crowd/crowd.pdf How to solve the digital commons dilemma. attention social networks reputation crowdsourcing The tragedy of the digital commons does not seem to prevent the copious voluntary production of content that one witnesses in the web. We show through an analysis of a massive data set from Youtube that the productivity exhibited in crowdsourcing exhibits a strong positive dependence on attention, measured by the number of downloads. Conversely, a lack of attention leads to a decrease in the number of videos uploaded and the consequent drop in productivity, which in many cases asymptotes to no uploads whatsoever. Moreover, we observed that uploaders compare themselves to others when having low productivity and to themselves when exceeding a personal threshold. Bernardo A. Huberman, Daniel M. Romero and Fang Wu 2008-09-11 12:00:00 http://www.hpl.hp.com/research/scl/papers/howopinions/wine.pdf How web discourse evolves. To appear in the Proceedings of the Workshop on Internet and Network Economics-2008 opinion formation social networks polarization crowdsourcing WINE No aspect of the massive participation in content creation that the web enables is more evident than in the countless number of opinions, news and product reviews that are constantly posted on the Internet. Given their importance we have analyzed their temporal evo- lution in a number of scenarios. We have found that while ignorance of previous views leads to a uniform sampling of the range of opinions among a community, exposure of previous opinions to potential review- ers induces a trend following process which leads to the expression of increasingly extreme views. Moreover, when the expression of an opinion is costly and previous views are known, a selection bias softens the ex- treme views, as people exhibit a tendency to speak out differently from previous opinions. These findings are not only robust but also suggest simple procedures to extract given types of opinions from the population at large. Fang Wu and Bernardo A. Huberman 2008-09-11 12:00:00 Dennis M. Wilkinson 2008-04-10 00:00:00 social networks attention opinion formation 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 reinfo cement 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. Simple behavioral rules hold across hundreds of millions of contributions to disparate online peer production efforts. http://www.hpl.hp.com/research/scl/papers/regularities/ Scott A. Golder 2008-04-09 00:00:00 The ease and lack of cost associated with taking digital photographs have allowed people to amass large personal photograph collections. These collections contain valuable information about their owners' social relationships. This paper is a preliminary investigation into how digital photo collections can provide useful data for the study of social networks. Results from an analysis of 23 subjects photo collections demonstrate the feasibility of this approach. The relationship between perceived closeness and network position, as well as future questions, are also discussed. Digital photo archives contain valuable information about individuals' social networks. social networks photos HT http://www.hpl.hp.com/research/scl/papers/sna-photos/ Tad Hogg and Gabor Szabo 2008-03-25 0:00:00 Web sites where users create and rate content as well as form networks with other users display long-tailed distributions in many aspects of behavior. Using behavior on one such community site, Essembly, we propose and evaluate plausible mechanisms to explain these behaviors. Unlike purely descriptive models, these mechanisms rely on user behaviors based on information available locally to each user. For Essembly, we find the long-tails arise from large differences among user activity rates and qualities of the rated content, as well as the extensive variability in the time users devote to the site. We show that the models not only explain overall behavior but also allow estimating the quality of content from their early behaviors. http://www.hpl.hp.com/research/idl/papers/diversity/ Diversity among users and the content they create in the Essembly web site. social networks essembly Tad Hogg, Gabor Szabo, Dennis M. Wilkinson, and Michael J. Brzozowski 2008-01-12 0:00:00 Traditionally, online social network sites like Facebook and MySpace allow people to form links to "friends" but do little to qualify the semantic meaning of the friendship. As a result, many users "collect" friends on these sites, conflating "acquaintances" with "friends". Essembly, a "fiercely non-partisan social network", on the other hand, lets its users enrich the meaning of their relations to others by explicitly labeling them "friends", "allies", or "nemeses". Essembly then allows its members to post resolves reflecting controversial opinions on political issues. As a defining activity on the site, members can vote on these resolves on a four-point scale ranging from complete agreement to full opposition. We examined how the uncommon link semantics affects users in casting their votes. In particular, Essembly prominently highlights the activities of users' acquaintances, and the question arises if this makes them more likely to participate, and if so, how this information affects votes. It is widely assumed that social networks enhance, if not drive, the popularity of online services; what does an additional layer of link classification add to them? Papers appeared at CHI 2008 and AAAI Spring Symposium on Social Information Processing 2008. http://www.hpl.hp.com/research/idl/papers/essembly Examines the usefulness of distinguishing between friends and similar/dissimilar users in propagating new content in an online social network, and suggests resulting design implications for social content aggregation services and recommender systems. social networks voting essembly influence CHI http://www.hpl.hp.com/research/idl/papers/facebook/index.html There are strong temporal patterns of communication among the millions of people interacting through Facebook. We have analyzed the fully anonymized headers of 362 million messages exchanged by 4.2 million users of Facebook, an online social network of college students, during a 26 month interval. The data reveal a number of strong daily and weekly regularities which provide insights into the time use of college studen s and their social lives, including seasonal variations. We also examined how factors such as school affiliation and informal online "friend" lists affect the observed behavior and temporal patterns. Finally, we show that Facebook users appear to be clustered by school with respect to their temporal messaging patterns. Full citation: Scott A. Golder, Dennis Wilkinson and Bernardo A. Huberman. "Rhythms of Social Interaction: Messaging within a Massive Online Network" 3rd International Conference on Communities and Technologies (CT2007). East Lansing, MI. June 28-30, 2007. Scott Golder, Dennis Wilkinson and Bernardo A. Huberman CT facebook social networks temporal patterns 2007-01-26 01:50:00