Evidence map›Paper›PMID 41935064›Full record

ArticleScientific reports2026

Public opinion dissemination simulation based on large language model multi-agent systems.

Hai Lan, Hua Hu, Peng Cheng Guo, Qi Huang

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Hai LanSchool of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.
Hua HuFiberHome Telecommunication Technologies Co., Ltd, Wuhan, 430073, Hubei, China. huhua@fiberhome.com.
Peng Cheng GuoFiberHome Telecommunication Technologies Co., Ltd, Wuhan, 430073, Hubei, China.
Qi HuangFiberHome Telecommunication Technologies Co., Ltd, Wuhan, 430073, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Internet has fundamentally reshaped the formation and diffusion of public opinion in modern society. However, existing simulation studies often face challenges such as insufficient fidelity in evolutionary dynamics, behavioral homogenization, and high modeling costs. This study develops a realistic public opinion simulation system that integrates macro-level diffusion patterns with micro-level individual cognition, addressing the traditional models' limitations in generalizability and high resource demands. This study proposes an LLM-based multi-agent simulation framework for public opinion dissemination. The framework first constructs behavior probability profiles calibrated against real-world social media data, following exponential and normal distribution models to constrain agent behavior frequency and topical tendencies (macro-dynamics). Concurrently, by utilizing an LLM as the cognitive core, the system enables dynamic semantic content generation via the integration of Public Opinion Simulation Standard Operating Procedure (PSOP) and a Global Information Sharing Pool (GISP) (micro-cognition). Comparative experiments across two distinct scenarios-"Public Policy" and "Food Safety"-show that (1): The framework exhibits significant cross-scenario robustness, replicating the incubation-eruption-decay' evolutionary pattern, which aligns with propagation dynamics without the need for parameter adjustment (2); Quantitative evaluation shows that the normalized agent behavior distribution entropy reaches 0.69; moreover the Distinct-2 metric for generated content reaches 0.83, substantially mitigating behavioral homogenization (3); Compared with traditional deep learning methods, this framework supports zero-shot cold start, with negligible computational costs for domain adaptation. By unifying the agent architecture, this research enables efficient simulation of public opinion evolution, providing a low-cost, high-fidelity methodological paradigm for public opinion crisis management.

Indexed as

Information DisseminationPublic OpinionComputer SimulationHumansLarge Language ModelsSocial MediaAgentLarge Language Model (LLM)Multi-Agent SystemProbabilistic Graphical ModelPublic Opinion Simulation

Identifiers

PMID41935064
PMCPMC13201537

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.