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

arXiv: https://arxiv.org/abs/2601.13932 · HTML: https://mlsanity.com/paper/2601.13932/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 50 references that are in the map.
- First posted on arXiv 20 Jan 2026.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Arctic-Embed 2.0: Multilingual Retrieval Without Compromise](https://mlsanity.com/paper/2412.04506/) (2024)
- [Statistical physics of human cooperation](https://mlsanity.com/paper/1705.07161/) (2017)
- [Modeling echo chambers and polarization dynamics in social networks](https://mlsanity.com/paper/1906.12325/) (2019)
- [Social physics](https://mlsanity.com/paper/2110.01866/) (2021)
- [Emergence of polarized ideological opinions in multidimensional topic spaces](https://mlsanity.com/paper/2007.00601/) (2020)

## Abstract (arXiv)

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 ferromagnetic-like phase (global consensus), while β < 0 induces an antiferromagnetic-like 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.