Providing awareness, understanding and control of personalized stream filtering in a P2P social network

Abstract

In Online Social Networks (OSNs) users are often 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 OSNs, such as Facebook. Unfortunately, personalized filtering leads to "the filter bubble" problem where the user is trapped inside a world within the limited boundaries of her interests and cannot be exposed to any surprising, desirable information. Moreover, these OSNs are black boxes, providing no transparency of how the filtering mechanism decides what is to be shown in the social data stream. As a result, the user trust in the system can decline. This paper proposes an interactive method to visualize the personalized stream filtering in OSNs. The proposed visualization helps to create awareness, understanding, and control of personalized stream filtering to alleviate "the filter bubble" problem and increase the users' trust in the system. The visualization is implemented in MADMICA - a privacy aware decentralized OSN, based on the Friendica P2P protocol. We present the results of a small-scale study to evaluate the user experience with the proposed visualization in MADMICA. © Springer-Verlag 2013.