Emergence of polarized ideological opinions in multidimensional topic spaces
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Abstract (arXiv)
Opinion polarization is on the rise, causing concerns for the openness of public debates. Additionally, extreme opinions on different topics often show significant correlations. The dynamics leading to these polarized ideological opinions pose a challenge: How can such correlations emerge, without assuming them a priori in the individual preferences or in a preexisting social structure? Here we propose a simple model that qualitatively reproduces ideological opinion states found in survey data, even between rather unrelated, but sufficiently controversial, topics. Inspired by skew coordinate systems recently proposed in natural language processing models, we solidify these intuitions in a formalism of opinions unfolding in a multidimensional space where topics form a non-orthogonal basis. Opinions evolve according to the social interactions among the agents, which are ruled by homophily: two agents sharing similar opinions are more likely to interact. The model features phase transitions between a global consensus, opinion polarization, and ideological states. Interestingly, the ideological phase emerges by relaxing the assumption of an orthogonal basis of the topic space, i.e. if topics thematically overlap. Furthermore, we analytically and numerically show that these transitions are driven by the controversialness of the topics discussed, the more controversial the topics, the more likely are opinion to be correlated. Our findings shed light upon the mechanisms driving the emergence of ideology in the formation of opinions.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jul 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2; 2025: 4; 2026: 1.
9 papers.
- 2025AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Societycited by 180
- 2021Learning Ideological Embeddings from Information Cascadescited by 6
- 2022Predicting Opinion Dynamics via Sociologically-Informed Neural Networkscited by 5
- 2024Variational Inference of Parameters in Opinion Dynamics Modelscited by 3
- 2024Towards Opinion Shaping: A Deep Reinforcement Learning Approach in Bot-User Interactionscited by 1
- 2025Dynamics of Algorithmic Content Amplification on TikTokcited by 1
- 2025Uncovering Social Network Activity Using Joint User and Topic Interactioncited by 0
- 2025Constructing Political Coordinates: Aggregating Over the Opposition for Diverse News Recommendationcited by 0
- 2026Generating consensus and dissent on massive discussion platforms with a semantic-vector modelcited by 0
- 2026Generating consensus and dissent on massive discussion platforms with a semantic-vector model20 Jan 2026
- 2025Constructing Political Coordinates: Aggregating Over the Opposition for Diverse News Recommendation14 Nov 2025
- 2025Uncovering Social Network Activity Using Joint User and Topic Interaction15 Jun 2025
- 2025Dynamics of Algorithmic Content Amplification on TikTok26 Mar 2025
- 2025AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society12 Feb 2025
- 2024Towards Opinion Shaping: A Deep Reinforcement Learning Approach in Bot-User Interactions12 Sep 2024
- 2024Variational Inference of Parameters in Opinion Dynamics Models8 Mar 2024
- 2022Predicting Opinion Dynamics via Sociologically-Informed Neural Networks7 Jul 2022
- 2021Learning Ideological Embeddings from Information Cascades28 Sep 2021