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

In Online Social Networks (OSNs), users are often overwhelmed with a huge amount of social data, most of which are irrelevant to their interest. Filtering of the social data stream is the common way to deal with this problem, and it has already been applied by OSNs, such as Facebook and Google+. 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 for the user about how the filtering mechanism decides what is to be shown in the activity stream. As a result, the user trust in the system can decline. This thesis presents an interactive method to visualize the personalized stream filtering in OSNs. The proposed visualization helps to create awareness, explanation, 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 new privacy-aware decentralized OSN, based on the Friendica P2P protocol, which filters the social updates stream of users based on their interests. The results of three user evaluations are presented in this thesis: small-scale pilot study, qualitative study and large-scale quantitative study with 326 participants. The results of the small-scale study show that the filter bubble visualization makes the users aware of the filtering mechanism, engages them in actions to correct and change it, and as a result, increases the users’ trust in the system. The qualitative study reveals a generally higher proportion of desirable user perceptions for the awareness, explanation and control of the filter bubble provided by the visualization. Moreover, the results of the quantitative study 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 the data stream they are seeing

Providing awareness, explanation and control of personalized filtering in a social networking site

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 “the 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 “filter 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

What am I not seeing? An Interactive Approach to Social Content Discovery in Microblogs

In this paper, we focus on the informational and user experience benefits of user-driven topic exploration in microblog communities, such as Twitter, in an inspectable, controllable and personalized manner. To this end, we introduce ``HopTopics'' -- a novel interactive tool for exploring content that is popular just beyond a user's typical information horizon in a microblog, as defined by the network of individuals that they are connected to. We present results of a user study (N=122) to evaluate HopTopics with varying complexity against a typical microblog feed in both personalized and non-personalized conditions. Results show that the HopTopics system, leveraging content from both the direct and extended network of a user, succeeds in giving users a better sense of control and transparency. Moreover, participants had a poor mental model for the degree of novel content discovered when presented with non-personalized data in the Inspectable interface