The "filter bubble" is a term which refers to people getting encapsulated in streams of data such as news or social network updates that are personalized to their interests. While people need protection from information overload and maybe prefer to see content they feel familiar or agree with, there is the danger that important issues that should be of concern for everyone will get filtered away and people will lack exposure to different views, living in "echo-chambers", blissfully unaware of the reality. We have proposed a design of an interactive visualization, which provides the user of a social networking site with awareness of the personalization mechanism (the semantics and the source of the content that is filtered away), and with means to control the filtering mechanism. The visualization has been implemented in a peer-to-peer social network, called MADMICA, and we present here the results of a large scale lab study with 163 crowd-sourced participants. The results demonstrate that the visualization leads to increased users' awareness of the filter bubble, understandability of the filtering mechanism and to a feeling of control over their data stream.

Social networking sites (SNSs) have applied personalized filtering to deal with overwhelmingly irrelevant social data. However, due to the focus of accuracy, the personalized filtering often leads to Bthe filter bubble^problem where the users can only receive information that matches their pre-stated preferences but fail to be exposed to new topics. Moreover, these SNSs are black boxes, providing no transparency for the user about how the filtering mechanism decides what is to be shown in the activity stream. As a result, the user's usage experience and trust in the system can decline. This paper presents an interactive method to visualize the personalized filtering in SNSs. The proposed visualization helps to create awareness, explanation, and control of personalized filtering to alleviate the Bfilter bubble^problem and increase the users' trust in the system. Three user evaluations are presented. The results show that users have a good understanding about the filter bubble visualization, and the visualization can increase users' awareness of the filter bubble, understandability of the filtering mechanism and to a feeling of control over the data stream they are seeing. The intuitiveness of the design is overall good, but a context sensitive help is also preferred. Moreover, the visualization can provide users with better usage experience and increase users' trust in the system.

No abstract

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 Madmica -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.