All major on-line social networks, such as MySpace, Facebook, LiveJournal, and Orkut, are built around the concept of friendship. It is not uncommon for a social network participant to have over 100 friends. A natural question arises: are they all real friends of hers, or does she mean something different when she calls them "friends?" Speaking in other words, what is the relationship between off-line (real, traditional) friendship and its on-line (virtual) namesake? In this paper, we use sociological data to suggest that there is a significant difference between the concepts of virtual and real friendships. We further investigate the structure of on-line friendship and observe that it follows the Pareto (or double Pareto) distribution and is subject to age stratification but not to gender segregation. We introduce the concept of digital personality that quantifies the willingness of a social network participant to engage in virtual friendships.
The mechanisms of friendship'' allow the users to establish new connections and enable social searching (locating and maintaining offline connections online, in a virtual setting, to learn more about them, date with them, or even engage in casual sex) @cite_0 . It has been shown that through friendships'' users impact their friends' decisions @cite_14 , affect the predictability of the friends' actions and adopt behaviours exhibited by their friends @cite_1 , and influence the behavior patterns of their friends @cite_15 @cite_9 .
@cite_14 @cite_9 @cite_1 @cite_0 @cite_15
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Traditional online social network sites use a single monolithic "friends" relationship to link users. However, users may have more in common with strangers, suggesting the use of a "similarity network" to recommend content. This paper examines the usefulness of this distinction in propagating new content. Using both macroscopic and microscopic social dynamics, we present an analysis of Essembly, an ideological social network that semantically distinguishes between friends and ideological allies and nemeses. Although users have greater similarity with their allies than their friends and nemeses, surprisingly, the allies network does not affect voting behavior, despite being as large as the friends network. In contrast, users are influenced differently by their friends and nemeses, indicating that people use these networks for distinct purposes. We suggest resulting design implications for social content aggregation services and recommender systems. Characterizing the relationship that exists between a person's social group and his her personal behavior has been a long standing goal of social network analysts. In this paper, we apply data mining techniques to study this relationship for a population of over 10 million people, by turning to online sources of data. The analysis reveals that people who chat with each other (using instant messaging) are more likely to share interests (their Web searches are the same or topically similar). The more time they spend talking, the stronger this relationship is. People who chat with each other are also more likely to share other personal characteristics, such as their age and location (and, they are likely to be of opposite gender). Similar findings hold for people who do not necessarily talk to each other but do have a friend in common. Our analysis is based on a well-defined mathematical formulation of the problem, and is the largest such study we are aware of. Large numbers of college students have become avid Facebook users in a short period of time. In this paper, we explore whether these students are using Facebook to find new people in their offline communities or to learn more about people they initially meet offline. Our data suggest that users are largely employing Facebook to learn more about people they meet offline, and are less likely to use the site to initiate new connections. Online social networks pose an interesting problem: how to best characterize the different classes of user behavior. Traditionally, user behavior characterization methods, based on user individual features, are not appropriate for online networking sites. In these environments, users interact with the site and with other users through a series of multiple interfaces that let them to upload and view content, choose friends, rank favorite content, subscribe to users and do many other interactions. Different interaction patterns can be observed for different groups of users. In this paper, we propose a methodology for characterizing and identifying user behaviors in online social networks. First, we crawled data from YouTube and used a clustering algorithm to group users that share similar behavioral pattern. Next, we have shown that attributes that stem from the user social interactions, in contrast to attributes relative to each individual user, are good discriminators and allow the identification of relevant user behaviors. Finally, we present and discuss experimental results of the use of proposed methodology. A set of useful profiles, derived from the analysis of the YouTube sample is presented. The identification of different classes of user behavior has the potential to improve, for instance, recommendation systems for advertisements in online social networks.