RePBubLik: Reducing the Polarized Bubble Radius with Link Insertions
arXiv:2101.04751 · doi:10.1145/3437963.3441825
Abstract
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 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.
References in corpus (6)
- The Dynamics of Viral Marketing
- Community Interaction and Conflict on the Web
- Measuring Political Polarization: Twitter shows the two sides of Venezuela
- Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems
- Opinion Exchange Dynamics
- Reconstructing directed networks for better synchronization
Cited by in corpus (6)
- Network polarization, filter bubbles, and echo chambers: An annotated review of measures and reduction methods
- Rewiring What-to-Watch-Next Recommendations to Reduce Radicalization Pathways
- Local Edge Dynamics and Opinion Polarization
- Diameter Minimization by Shortcutting with Degree Constraints
- A Nearly-Linear Time Algorithm for Minimizing Risk of Conflict in Social Networks
- How Inclusive Are Wikipedia's Hyperlinks in Articles Covering Polarizing Topics?