Opinion Polarization in LLM-Based Social Networks: Manipulation and Mitigation

Ahad N. Zehmakan (Australian National University); Ali Safarpoor Dehkordi (Australian National University); Mohammad Shirzadi (Australian National University)

arxiv: 2606.18795 · v1 · pith:PPBVMRQMnew · submitted 2026-06-17 · 💻 cs.SI

Opinion Polarization in LLM-Based Social Networks: Manipulation and Mitigation

Ali Safarpoor Dehkordi (Australian National University) , Mohammad Shirzadi (Australian National University) , Ahad N. Zehmakan (Australian National University) This is my paper

Pith reviewed 2026-06-26 19:10 UTC · model grok-4.3

classification 💻 cs.SI

keywords opinion polarizationLLM-based social networksadversarial manipulationmitigation strategiesopinion dynamicsnatural language interactionssocial network simulations

0 comments

The pith

Even an adversary with a limited budget can considerably increase polarization in LLM-based social networks, and common mitigations do not restore baseline levels.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines vulnerability to polarization in social networks simulated by large language model agents that have assigned personas and exchange natural language posts. It shows that an adversary can drive up polarization even when the manipulation budget is restricted. Reactive defenses that assign specific users to counter attacks and proactive defenses that build general resistance both lower the polarization increase but leave the network more polarized than before the attack. A reader would care because these richer simulations capture context-dependent responses that simpler mathematical models omit, suggesting real online networks may carry similar risks.

Core claim

In a framework where LLM agents with diverse personas interact over a social network by exchanging natural language posts and updating their opinions accordingly, even an adversary with a limited manipulation budget can considerably increase polarization. Reactive mitigations, which assign specific users to actively counter manipulation, and proactive interventions, which increase resistance through general mechanisms, both reduce the impact of adversarial attacks but generally do not restore the network to its baseline polarization state.

What carries the argument

LLM agents with assigned diverse personas that exchange natural language posts and update opinions in a context-dependent manner.

Load-bearing premise

LLM agents with assigned personas produce opinion-update dynamics that are representative of real human social networks.

What would settle it

Running the same limited-budget manipulation experiment with actual human participants in a controlled network and finding no polarization increase would falsify the central claim.

Figures

Figures reproduced from arXiv: 2606.18795 by Ahad N. Zehmakan (Australian National University), Ali Safarpoor Dehkordi (Australian National University), Mohammad Shirzadi (Australian National University).

**Figure 1.** Figure 1: Illustration of the reactive and proactive mitigations considered in this work. Plots (i) and (ii) show reactive mitigations, where node colors represent agent opinions and message colors represent content stances: blue and red indicate opposing viewpoints, darker shades indicate more extreme opinions or stances, and white denotes neutrality. Plots (iii)–(vii) show proactive mechanisms that modify expos… view at source ↗

**Figure 3.** Figure 3: Effect of changing the manipulator budget. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: Reactive and proactive mitigations with [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗

**Figure 5.** Figure 5: Susceptible versus persistent manipulators in existence of different mitigations. [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗

**Figure 7.** Figure 7: Comparison across different LLMs. The first row corresponds to DeepSeek and the second row corresponds to GPT-4o-mini. more diverse behaviors on this dataset. This is reflected in the greater variety of trends observed on Reddit compared to Twitter. One possible explanation is the difference in graph structure. The Twitter dataset appears to have more realistic social-network characteristics, with cleare… view at source ↗

**Figure 8.** Figure 8: Comparison of the Reddit and Twitter real-world datasets. Conclusion In this study, we investigated adversarial opinion manipulation in social networks using an LLM-based simulation framework that captures language-based interactions. Our results show that social networks are highly vulnerable to manipulation; even a small number of adversarial manipulators can noticeably increase polarization and extr… view at source ↗

read the original abstract

How vulnerable are online social networks to adversaries who seek to amplify opinion polarization by manipulating opinions, and how difficult is it to mitigate such manipulation? Existing studies have examined this question using mathematical models of opinion dynamics. While these models offer valuable theoretical insights, they rely on simplified assumptions about interactions, message content, and opinion updates, limiting the adversarial strategies they can capture and the applicability of their findings to real-world settings. Large language model (LLM)-based simulations provide a richer alternative: agents can be assigned diverse personas, communicate through natural language, and respond to persuasive or adversarial content in a context-dependent way. This enables the study of manipulation strategies that are difficult to represent using classical mathematical models. To the best of our knowledge, this study provides the first systematic analysis of polarization amplification and mitigation in an LLM-based simulated social network framework. In our framework, LLM agents with diverse personas interact over a social network by exchanging natural language posts and updating their opinions accordingly. We show that even an adversary with a limited manipulation budget can considerably increase polarization. We then study two classes of defense mechanisms: reactive mitigations, which assign specific users to actively counter manipulation, and proactive interventions, which increase resistance through general mechanisms not tied to particular users. Our results show that although these mechanisms reduce the impact of adversarial attacks, they generally do not restore the network to its baseline polarization state. These findings suggest that neither approach fully overcomes the vulnerability of the network, highlighting the potential risk of such attacks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

LLM agent simulations show limited-budget manipulation increases polarization and standard mitigations fall short, but the work provides no validation that these agents match human opinion dynamics.

read the letter

The main finding is that in this LLM-based network simulation, an adversary with a small manipulation budget can push polarization up noticeably, while both reactive counters and proactive resistance measures cut the effect but leave the network more polarized than the no-attack baseline.

The paper does one thing clearly: it replaces the usual simplified math models with agents that have personas, post in natural language, and update opinions in a context-sensitive way. That lets them test manipulation tactics that are hard to encode in equations, and the abstract frames it as the first systematic run of this kind.

The gaps are substantial. The abstract supplies zero information on which LLMs are used, how opinions are actually updated from posts, what polarization metric is applied, what the network looks like, or how the manipulation budget is defined. More importantly, there is no comparison of the simulated trajectories or mitigation outcomes against human-subject data or established psychological models of opinion change. The claim that these results speak to real social networks therefore rests on an untested modeling assumption.

This is for researchers working on computational social science and AI platform governance who want to see richer simulations. A reader looking for actionable evidence on manipulation risks will find the current version too thin to rely on.

It should go to peer review. The core idea is worth developing, but the execution details and validation steps need to be supplied and checked before the conclusions can be assessed.

Referee Report

2 major / 1 minor

Summary. The paper introduces an LLM-based simulation framework in which agents assigned diverse personas interact over a social network by exchanging natural-language posts and updating opinions in a context-dependent manner. It claims to provide the first systematic analysis of adversarial manipulation, showing that an adversary with a limited manipulation budget can considerably increase polarization, and that both reactive mitigations (assigning specific users to counter attacks) and proactive interventions (general resistance mechanisms) reduce the impact of attacks but generally fail to restore the network to its baseline polarization state.

Significance. If the LLM simulation dynamics prove representative, the framework enables study of richer adversarial strategies and natural-language persuasion effects that are difficult to encode in classical mathematical opinion-dynamics models. The explicit finding that neither reactive nor proactive defenses fully restore baseline polarization would be a useful contribution to understanding platform vulnerabilities. The work correctly highlights the limitations of simplified mathematical models and positions the LLM approach as a more flexible alternative.

major comments (2)

[Abstract (framework description)] Abstract (framework description): No information is given on the specific LLMs used, the opinion-update rule, the polarization metric, the network topology, the manipulation budget definition, or statistical controls; therefore the data-to-claim link cannot be evaluated. This is load-bearing for the central claims that limited-budget adversaries considerably increase polarization and that mitigations reduce but do not restore baseline levels.
[Abstract (framework description)] Abstract (framework description): The assumption that LLM agents with assigned personas exchanging natural-language posts produce opinion-update dynamics sufficiently representative of real human social networks is stated without any empirical anchoring—no comparison of simulated polarization trajectories, update rules, or mitigation effects to human-subject data, field studies, or established psychological models. This unvalidated modeling choice is load-bearing for conclusions about real-world manipulation vulnerability and mitigation effectiveness.

minor comments (1)

[Abstract] The abstract would benefit from a concise statement of the number of agents, simulation duration, and number of independent runs to allow readers to gauge the scale of the reported effects.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and outline the revisions we will make.

read point-by-point responses

Referee: Abstract (framework description): No information is given on the specific LLMs used, the opinion-update rule, the polarization metric, the network topology, the manipulation budget definition, or statistical controls; therefore the data-to-claim link cannot be evaluated. This is load-bearing for the central claims that limited-budget adversaries considerably increase polarization and that mitigations reduce but do not restore baseline levels.

Authors: We agree that the abstract is high-level and omits these implementation details, which are described in the Methods section of the full manuscript. To address the concern, we will revise the abstract to include concise references to the LLMs employed, the opinion-update rule, the polarization metric, network topology, manipulation budget definition, and statistical controls. This will strengthen the data-to-claim connection while preserving the abstract's brevity. revision: yes
Referee: Abstract (framework description): The assumption that LLM agents with assigned personas exchanging natural-language posts produce opinion-update dynamics sufficiently representative of real human social networks is stated without any empirical anchoring—no comparison of simulated polarization trajectories, update rules, or mitigation effects to human-subject data, field studies, or established psychological models. This unvalidated modeling choice is load-bearing for conclusions about real-world manipulation vulnerability and mitigation effectiveness.

Authors: The referee correctly identifies that the manuscript provides no empirical anchoring to human data. Our work introduces an LLM-based simulation framework to explore richer dynamics than mathematical models permit; it is not presented as a validated model of human behavior. We will revise the abstract and add an explicit Limitations section to state the modeling assumptions, note the absence of direct human-subject validation, and discuss implications for interpreting real-world applicability. This will make the scope transparent. revision: yes

Circularity Check

0 steps flagged

No circularity: results are simulation outputs, not definitional reductions

full rationale

The paper presents a simulation framework in which LLM agents with personas exchange natural-language posts and update opinions; all central claims (adversary increases polarization; mitigations reduce but do not restore baseline) are stated as direct outcomes of running those simulations. No equations, fitted parameters, self-citations, or uniqueness theorems appear in the provided text that would reduce any result to an input by construction. The modeling assumption about LLM representativeness is an external validity concern, not a circularity in the derivation chain itself.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Only the abstract is available, so the ledger records the high-level modeling choices described there; no numerical parameters or formal axioms are stated.

axioms (1)

domain assumption LLM agents with diverse personas exchanging natural language posts produce opinion dynamics representative enough of human networks to support claims about real-world manipulation risk.
This premise is required for the simulation results to inform real social-network vulnerability.

invented entities (1)

LLM-based social network simulation framework no independent evidence
purpose: To enable richer adversarial strategies and context-dependent opinion updates than classical mathematical models allow.
The framework is introduced as the core methodological contribution.

pith-pipeline@v0.9.1-grok · 5820 in / 1215 out tokens · 24059 ms · 2026-06-26T19:10:17.311263+00:00 · methodology

discussion (0)

Reference graph

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