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Generating consensus and dissent on massive discussion platforms with a semantic-vector model

Alfredo Ferrer1,2, David Muñoz-Jordán1,*, Alejandro Rivero1,2, Alfonso Tarancón3, Carlos Tarancón2, and David Yllanes1,3,4,5

  • *Contact author: dmunoz@bifi.es
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Phys. Rev. E 114, 014311 – Published 21 July, 2026

DOI: https://doi.org/10.1103/kmg5-zhmr

Abstract

Reaching consensus on massive discussion networks is critical for reducing noise and achieving optimal collective outcomes. However, the natural tendency of humans to preserve their initial ideas constrains the emergence of global solutions. To address this, collective intelligence (CI) platforms facilitate the discovery of globally superior solutions. We introduce a dynamical system based on the standard O(N) model to drive the aggregation of semantically similar ideas. The system consists of users represented as nodes in a d=2 lattice with nearest-neighbor interactions, where their ideas are represented by semantic vectors computed with a pretrained embedding model. We analyze the system's equilibrium states as a function of the coupling parameter β. Our results show that β>0 drives the system toward a ferromagneticlike phase (global consensus), while β<0 induces an antiferromagneticlike state (maximum dissent), where users maximize semantic distance from their neighbors. This framework offers a controllable method for managing the tradeoff between cohesion and diversity in CI platforms.

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Outline

Information

Phys. Rev. E 114, 014311– Published 21 July, 2026
  • Received 21 January 2026
  • Revised 2 June 2026
  • Accepted 29 June 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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