Understanding and controlling the filter bubble through interactive visualization

Abstract

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.

Perspective-based search: a new paradigm for bursting the information bubble

“…Stracke (2014) suggested allowing the users to modify the amount of personalization in search by enabling or disabling categories of search signals (e.g., search history, browsing history, and geography). Nagulendra and Vassileva (2014) suggested visualizing the filter bubble and allowing the user to control what resides inside it and what does not. The user controls which categories to be inside the bubble and which users to be inside it for each category.…”

“…Many studies (Negroponte 1995;Munson and Resnick 2010;El-Arini et al 2012;Munson 2012;Munson et al 2013;Fu 2014a, 2014b;Nagulendra and Vassileva 2014;Liao et al 2015) focused on problems other than search, e.g., filtering/recommendation, browsing comments, or browsing news sites.…”

Knowing the unknown

“…One approach to cope with filter bubbles is to help users to better understand the recommendation space, by informing them about the compromises that are inherent in any set of recommendations, relative to a wider set of items. In this regard, the work of [9] is pertinent, showing how visualisation could increase user awareness of the filter bubble, understandability of the filtering mechanism, and a user's sense of control over their data stream. In another study users were able to control which people in their immediate and extended network contributed to their information feed on Twitter, and the findings suggest that the interface increased users' sense of transparency and control [8].…”

Same, Same, but Different

“…In recent years, a common criticism of recommender systems has been that they may serve to create filter bubbles (see [20]) for users by censoring their choices over time and effectively polarising their preferences; see [3,16,18]. This is however disputed, for example, Flaxman et al found evidence that recent technological changes both increase and decrease various aspects of polarisation [7].…”