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Reversing Polarization through Tolerance-Aware Recommendation and Adaptive Diversification — arXiv:2610.40289

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.40289
License (as archived): CC0-1.0
Archived: 2026-11-01T02:21:03.976Z
SHA-256 of the archived text: 5cc6d08cbc760eef8c78b1a9b225ef90e9e8073eb2f8cd2773b4ea90fada5253
Stable URL of this page: https://metraipolis.com/documento/dc_00a8274977ce

How to cite

Reversing Polarization through Tolerance-Aware Recommendation and Adaptive Diversification — arXiv:2610.40289 — arXiv (en), https://arxiv.org/abs/2610.40289 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_00a8274977ce (Metraipolis archive dc_00a8274977ce, sha256 5cc6d08cbc760eef8c78b1a9b225ef90e9e8073eb2f8cd2773b4ea90fada5253)

BibTeX

@misc{dc00a8274977ce,
  title = {Reversing Polarization through Tolerance-Aware Recommendation and Adaptive Diversification — arXiv:2610.40289},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.40289},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_00a8274977ce; sha256 5cc6d08cbc760eef8c78b1a9b225ef90e9e8073eb2f8cd2773b4ea90fada5253},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.40289. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Recommender systems face a tension between broadening exposure to diverse viewpoints and avoiding interactions that reinforce extreme positions. This tension is especially consequential in already polarized populations. Here we investigate whether recommendation strategies can reverse polarization rather than merely slow its development. Building on the Attraction--Repulsion Model of opinion dynamics, we introduce two complementary design principles: Tolerance-aware recommendation, which seeks to learn each individual's tolerance for disagreement and reduces exposure to viewpoints beyond that tolerance, and adaptive diversification, which broadens exposure for agents with more extreme opinions while retaining stronger personalization for moderates. Agent-based experiments show that highly personalized recommendations intensify polarization under polarized initial conditions. Tolerance awareness alone primarily slows polarization, whereas combining it with adaptive diversification can reduce polarization below its initial level and, under suitable conditions, restore consensus. Additional experiments incorporating persistent extreme influencers and a spiral-of-silence mechanism show that the combined strategy can mitigate polarization under social pressure and selective participation. To examine whether these findings extend beyond predefined opinion-update rules, we repeat the experiments on a simulated social platform populated by 100 LLM-driven agents prompted to move toward opinions similar to their own and away from those that differ substantially. Across three runs, the combined strategy is again the only recommendation policy tested that reverses polarization in every run. These findings distinguish polarization prevention from reversal and identify tolerance-aware, adaptive exposure as a promising recommendation design principle for reconnecting divided populations.

Tema degli esploratori: governance digitale e beni comuni (elenco arXiv cs.SI, nuovi lavori del giorno).
Categorie arXiv: Social and Information Networks (cs.SI).
Pagina del lavoro: https://arxiv.org/abs/2610.40289 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-01T02:11:58.375Z dalla pagina https://arxiv.org/list/cs.SI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).