Social relationships are a natural basis on which humans make trust decisions. Online Social Networks (OSNs) are increasingly often used to let users base trust decisions on the existence and the strength of social relationships. While most OSNs allow users to discover the length of the social path to other users, they do so in a centralized way, thus requiring them to rely on the service provider and reveal their interest in each other. This paper presents Social PaL, a system supporting the privacy-preserving discovery of arbitrary-length social paths between any two social network users. We overcome the bootstrapping problem encountered in all related prior work, demonstrating that Social PaL allows its users to find all paths of length two and to discover a significant fraction of longer paths, even when only a small fraction of OSN users is in the Social PaL system - e.g., discovering 70 of all paths with only 40 of the users. We implement Social PaL using a scalable server-side architecture and a modular Android client library, allowing developers to seamlessly integrate it into their apps.
OSN Properties. Another line of work has studied properties of OSNs. @cite_0 and @cite_4 study the structure of Facebook social graph, revealing that the average social path length suggested by the small world experiment" @cite_12 (i.e., six) does not apply for Facebook, as the majority of people are separated by a 4-hop path. @cite_21 define the relationship between tie strengths (i.e., the importance of a social relationship between two users) and various variables retrieved from the OSN social graph. @cite_32 , investigate the link between the tie strength definition (given by Granovotter @cite_17 ) and a composition of factors describing the emotional closeness in online relationships. They demonstrate the existence of the (i.e., the maximum number of people a user can actively interact with) for Facebook. In follow-up work @cite_46 @cite_13 , they also show the existence of four hierarchical layers of social relationships inside ego networks. Existence of the Dunbar number is also shown for Twitter in @cite_50 . Finally, Saram " a @cite_3 find an uneven distribution of tie strengths within ego networks that is characterized by the presence of a few strong and a majority of weak ties.
@cite_4 @cite_21 @cite_32 @cite_3 @cite_0 @cite_50 @cite_46 @cite_13 @cite_12 @cite_17
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The widespread use of online social networks, such as Facebook and Twitter, is generating a growing amount of accessible data concerning social relationships. The aim of this work is twofold. First, we present a detailed analysis of a real Facebook data set aimed at characterising the properties of human social relationships in online environments. We find that certain properties of online social networks appear to be similar to those found “offline” (i.e., on human social networks maintained without the use of social networking sites). Our experimental results indicate that on Facebook there is a limited number of social relationships an individual can actively maintain and this number is close to the well-known Dunbar’s number (150) found in offline social networks. Second, we also present a number of linear models that predict tie strength (the key figure to quantitatively represent the importance of social relationships) from a reduced set of observable Facebook variables. Specifically, we are able to predict with good accuracy (i.e., higher than 80 ) the strength of social ties by exploiting only four variables describing different aspects of users interaction on Facebook. We find that the recency of contact between individuals – used in other studies as the unique estimator of tie strength – has the highest relevance in the prediction of tie strength. Nevertheless, using it in combination with other observable quantities, such as indices about the social similarity between people, can lead to more accurate predictions 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. Due to the amount of personally identiable information shared by users of online social networks (OSNs) and the often not adequately adjusted privacy settings, it is possible to identify a user’s several OSN proles. In this paper, we illustrate that third parties just have to take a small step to link OSN proles of a user and, consequently, to aggregate various pieces of information shared in several OSNs. Particularly, based on statistical results we illustrate how the often publicly available friends lists can be exploited to link several OSN proles of a single natural person. The results presented in this paper show that proles can be linked even at low cost, i.e., without complex correlation techniques or high computational power. To assess the risk of privacy leakage by prole linking, we, additionally, report how often specic pieces of information are made publicly available by users in four of the most popular OSNs. We show that users tend to publish dierent pieces of information in dierent OSNs and, thereby, demonstrate that by linking friends lists more information about a user can be gained than the user shared in a single OSN. For the study we analyzed more than 180,000 user proles and compared more than seven million pairs of proles to investigate prole linkability. We have friends we consider very close and acquaintances we barely know. The social sciences use the term tie strength to denote this differential closeness with the people in our lives. In this paper, we explore how well a tie strength model developed for one social medium adapts to another. Specifically, we present a Twitter application called We Meddle which puts a Facebook tie strength model at the core of its design. We Meddle estimated tie strengths for more than 200,000 online relationships from people in 52 countries. We focus on the mapping of Facebook relational features to relational features in Twitter. By examining We Meddle's mistakes, we find that the Facebook tie strength model largely generalizes to Twitter. This is early evidence that important relational properties may manifest similarly across different social media, a finding that would allow new social media sites to build around relational findings from old ones.