RePBubLik: Reducing the Polarized Bubble Radius with Link Insertions

The topology of the hyperlink graph among pages expressing different opinions may influence the exposure of readers to diverse content. Structural bias may trap a reader in a polarized bubble with no access to other opinions. We model readers' behavior as random walks. A node is in a polarized bubble if the expected length of a random walk from it to a page of different opinion is large. The structural bias of a graph is the sum of the radii of highly-polarized bubbles. We study the problem of decreasing the structural bias through edge insertions. Healing all nodes with high polarized bubble radius is hard to approximate within a logarithmic factor, so we focus on finding the best $k$ edges to insert to maximally reduce the structural bias. We present RePBubLik, an algorithm that leverages a variant of the random walk closeness centrality to select the edges to insert. RePBubLik obtains, under mild conditions, a constant-factor approximation. It reduces the structural bias faster than existing edge-recommendation methods, including some designed to reduce the polarization of a graph

Wikipedia Polarization and Its Effects on Navigation Paths

Bias and polarization are not just about placing misinformation on the Web but also involve concerted efforts to change how we navigate it. One of the strongest points of Wikipedia is to allows readers to easily navigate a topic, through its hyperlinks structure. Thus, it is crucial to ensure a user to have the same probability of being exposed to knowledge that expresses different viewpoints concerning the given topic. In this work, we investigate whether the topology and polarization of a topic-induced-graph (e.g. U.S. Politics induced network) has an impact on users’ navigation paths making them biased toward one of the possible topic perspectives. Modeling users behaviour and exploiting Wikipedia clickstreams, we analyze users exposure to different leaning during their sessions, thus the chance of being trapped within a knowledge bubble presenting a unique viewpoint about the topic, and differences among users that start their navigation from articles representing different perspectives