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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Social media allow for an unprecedented amount of interaction between people online. A fundamental aspect of human social behavior, however, is the tendency of people to associate themselves with like-minded individuals, forming homogeneous social circles both online and offline. In this work, we apply a new model that allows us to distinguish between social ties of varying strength, and to observe evidence of homophily with regards to politics, music, health, residential sector & year in college, within the online and offline social network of 74 college students. We present a multiplex network approach to social tie strength, here applied to mobile communication data - calls, text messages, and co-location, allowing us to dimensionally identify relationships by considering the number of communication channels utilized between students. We find that strong social ties are characterized by maximal use of communication channels, while weak ties by minimal use. We are able to identify 75 of close friendships, 90 of weaker ties, and 90 of Facebook friendships as compared to reported ground truth. We then show that stronger ties exhibit greater profile similarity than weaker ones. Apart from high homogeneity in social circles with respect to political and health aspects, we observe strong homophily driven by music, residential sector and year in college. Despite Facebook friendship being highly dependent on residence and year, exposure to less homogeneous content can be found in the online rather than the offline social circles of students, most notably in political and music aspects. This paper intends to provide some insights of a scientific problem: how likely one's interests can be inferred from his her social connections -- friends, friends' friends, 3-degree friends, etc? Is "Birds of a Feather Flocks Together" a norm? We do not consider the friending activity on online social networking sites. Instead, we conduct this study by implementing a privacy-preserving large distribute social sensor system in a large global IT company to capture the multifaceted activities of 30,000+ people, including communications (e.g., emails, instant messaging, etc) and Web 2.0 activities (e.g., social bookmarking, file sharing, blogging, etc). These activities occupy the majority of employees' time in work, and thus, provide a high quality approximation to the real social connections of employees in the workplace context. In addition to such "informal networks", we investigated the "formal networks", such as their hierarchical structure, as well as the demographic profile data such as geography, job role, self-specified interests, etc. Because user ID matching across multiple sources on the Internet is very difficult, and most user activity logs have to be anonymized before they are processed, no prior studies could collect comparable multifaceted activity data of individuals. That makes this study unique. In this paper, we present a technique to predict the inference quality by utilizing (1) network analysis and network autocorrelation modeling of informal and formal networks, and (2) regression models to predict user interest inference quality from network characteristics. We verify our findings with experiments on both implicit user interests indicated by the content of communications or Web 2.0 activities, and explicit user interests specified in user profiles. We demonstrate that the inference quality prediction increases the inference quality of implicit interests by 42.8 , and inference quality of explicit interests by up to 101 . As online social networks expand their role beyond maintaining existing relationships, they may look to more faceted ratings to support the formation of new connections between their users. Our study focuses on one community employing faceted ratings, CouchSurfing.org, and combines data analysis of ratings, a large-scale survey, and in-depth interviews. In order to understand the ratings, we revisit the notions of friendship and trust and uncover an asymmetry: close friendship includes trust, but high levels of trust can be achieved without close friendship. To users, providing faceted ratings presents challenges, including differentiating and quantifying inherently subjective feelings such as friendship and trust, concern over a friend’s reaction to a rating, and knowledge of how ratings can affect others’ reputations. One consequence of these issues is the near absence of negative feedback, even though a small portion of actual experiences and privately held ratings are negative. We show how users take this into account when formulating and interpreting ratings, and discuss designs that could encourage more balanced feedback. Graphical virtual worlds add two new layers to the old question what determines friendship formation. First, it is possible to distinguish between off-line (player) and online (avatar) characteristics. Second, these environments offer new possibilities for studying friendship formation. By tracking friendship requests and their acceptance rate, researchers are able to distinguish between with whom players want to become friends and with whom they actually do become friends. This article examined friendship formation in Timik, a graphical virtual world targeted at Polish teenagers. Homophily, preferential attachment and status were tested as possible underlying mechanisms. Results showed that preferential attachment and status drove invitations: Players wanted to become friends with high-status players. However, high-status players were also more likely to reject offers. Homophily only played a minor role. Players preferred players of the same avatar class and similar age but of the opposite sex. Too simil...