Abstract-In this work we study the activity span of MySpace accounts and its connection to the distribution of the number of friends. The activity span is the time elapsed since the creation of the account until the user's last login time. We observe exponentially distributed activity spans. We also observe that the distribution of the number of friends over accounts with the same activity span is well approximated by a lognormal with a fairly light tail. These two findings shed light into the puzzling (yet unexplained) inflection point (knee) in the distribution of friends in MySpace when plotted in log-log scale. We argue that the inflection point resembles the inflection point of Reed's (Double Pareto) Geometric Brownian Motion with Exponential Stopping Times model. We also present evidence against the Dunbar number hypothesis of online social networks, which argues, without proof, that the inflection point is due to the Dunbar number (a theoretical limit on the number of people that a human brain can sustain active social contact with). While we answer many questions, we leave many others open.

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Abstract-Online social networks such as MySpace and Facebook have become popular platforms for people to make connections, share information, and interact with each other online. In an online social network, user publishing activities such as sending messages and posting photos, represent online interactions between friends. As more and more businesses use social networks as a means to propagate their "brand name" and distribute information about their product, a good understanding of user publishing characteristics is important for marketing analysis and aids in the ability to provide security measures for online social networks. In this work, we look at the implications of social networks with respect to commercial use. We are particularly interested in classifying commercial and personal profiles to protect the privacy and anonymity of individual users. We present an algorithm that uses online social network publishing relationships, such as publisher age distribution and usage patterns to construct a decision tree based classifier. The result is a C4.5 pruned decision tree which is applied to a PrivacyPreserving Data Publishing (PPDP) service to provide anonymity for online social network users.

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Abstract. This paper introduces an unobtrusive method and distributed solution set to aid users of on-line social networking sites, by creating a trusted environment in which every member has the ability to identify each other within their private social network by name, gender, age, location, and the specific usage patterns adopted by the group. Utopia protects members by understanding how the social network is created and the specific aspects of the group that make it unique and identifiable. The main focus of Utopia is the protection of the group, and their privacy within a social network from predators and spammers that characteristically do not fit within the well defined usage boundaries of the social network as a whole. The solution set provides defensive, as well as offensive tools to identify these threats. Once identified, client desktop tools are used to prevent these predators from further interaction within the group. In addition, offensive tools are used to determine the origin of the predator to allow actions to be taken by automated tools and law enforcement to alleviate the threat.