Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems

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Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems ( online-social-networks-measurement-analysis-and-applications )

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154 This algorithm has several desirable properties. It limits the amount of unwanted communication a sender can produce. At the same time, it allows an arbitrary amount of wanted communication. The algorithm limits the number of tokens a user can acquire before any of the associated communication is classified; thus, it limits the total amount of potentially unwanted communication a user can produce. Finally, the algorithm limits the number of tokens that can be issued for a specific recipient before that recipient classifies any of the associated communication; thus, an inactive or lazy user cannot cause senders to reserve a large number of credits, which would be bound until the communication were classified. 8.1.4 Credit adjustments Several issues, however, remain with the algorithm described so far. When a user’s credit balance reaches one of her credit bounds, she is, in effect, banned from produc- ing (in the case of the lower bound) or receiving (in the case of the upper bound) any further communication. What can cause a legitimate user’s credit balance to reach her bounds? Note that on the one hand, a user who receives unwanted communication earns credit. On the other hand, even a well-intentioned user may occasionally send communication to a recipient who considers it unwanted and therefore lose credit. Across all users, these effects balance out. However, unless a user, on average, re- ceives precisely the same amount of unwanted communication as she generates, her credit balance will eventually reach one of her bounds. As a result, legitimate users

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