Reducing Controversy by Connecting Opposing Views
Abstract
Society is often polarized by controversial issues that split the population into groups with opposing views. When such issues emerge on social media, we often observe the creation of `echo chambers', i.e., situations where like-minded people reinforce each other's opinion, but do not get exposed to the views of the opposing side. In this paper we study algorithmic techniques for bridging these chambers, and thus reduce controversy. Specifically, we represent the discussion on a controversial issue with an endorsement graph, and cast our problem as an edge-recommendation problem on this graph. The goal of the recommendation is to reduce the controversy score of the graph, which is measured by a recently-developed metric based on random walks. At the same time, we take into account the acceptance probability of the recommended edge, which represents how likely the edge is to materialize in the endorsement graph.
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Users are expected to be more likely to exchange information with peers sharing a similar leaning.
“Simple models of information spreading can gauge the presence of echo chambers: Users are expected to be more likely to exchange information with peers sharing a similar leaning (18, 42, 43).”
From The echo chamber effect on social media (2021) · cites this paper as [42]
Methods for identifying controversial topics can be used as building blocks for designing systems to reduce controversy on social media by connecting users with content outside their bubbles.
“Our methods for identifying controversial topics can be used as building blocks for designing such systems to reduce controversy on social media [21,22] by connecting social media users with content outside their own bubbles.”
From Quantifying controversy on social media (2018) · cites this paper as [21]
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- Authors
- Garimella, K., De Francisci Morales, G., Gionis, A., & Mathioudakis, M.
- Affiliations
- Aalto University (FI)
- Qatar Computing Research Institute, Doha, Qatar
- Helsinki Institute for Information Technology (FI)
- Venue
- Proceedings of the Tenth ACM International Conference on Web Search and Data Mining
- Publisher
- ACM
- Published
- 2017-02-02
- Pages
- 81-90
- OpenAlex
- W2585682652
- Cited by
- 165
- References
- 38
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 12 · 2023: 23 · 2022: 17 · 2021: 24 · 2020: 25 · 2019: 13 · 2018: 20 · 2017: 11
- FWCI
- 23.88
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Web Search and Data Mining (WSDM) (publishedVersion)
- Funders
- EC H2020 RIA (654024)
- Academy of Finland (286211)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- echo chambers, social polarization, social media, opinion polarization
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