Minimizing Social Data Overload through Interest-Based Stream Filtering in a P2P Social Network

In Online Social Networks (OSNs) users are overwhelmed with the huge amount of social data, most of which are irrelevant to their interest. Filtering of the social data stream is the way to deal with this problem, and it has already been applied by centralized OSNs, such as Facebook. However, it is much harder to filter the social data stream in decentralized OSNs. Decentralized OSNs, mostly based on P2P architectures, such as Diaspora or Friendica, have been proposed as an alternative to the currently dominant centralized OSNs, where people are forced to share their data with the site, and thus lose their control and rights over it. This paper presents an implementation of an interest based stream filtering mechanism using Mad mica - a decentralized OSN, based on the Friendica P2P protocol. The mechanism uses the interaction between users to construct a model of user interests overlaid on the relationships of users with their friends, which acts as a filter later while propagating social data. So Madmica provides a solution to two problems simultaneously - the problem of user privacy and control over their data (through its decentralized architecture) and the problem of social data overload (through its filtering mechanism). We present the results of a pilot study to evaluate the user experience with Madmica.