mlsanityData to 14 Oct 2026

Can AI automatically analyze public opinion? A LLM agents-based agentic pipeline for timely public opinion analysis

arXiv 2505.11401 ↗First posted 16 May 2025cs.CYcited by 2

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

This study proposes and implements the first LLM agents based agentic pipeline for multi task public opinion analysis. Unlike traditional methods, it offers an end-to-end, fully automated analytical workflow without requiring domain specific training data, manual annotation, or local deployment. The pipeline integrates advanced LLM capabilities into a low-cost, user-friendly framework suitable for resource constrained environments. It enables timely, integrated public opinion analysis through a single natural language query, making it accessible to non-expert users. To validate its effectiveness, the pipeline was applied to a real world case study of the 2025 U.S. China tariff dispute, where it analyzed 1,572 Weibo posts and generated a structured, multi part analytical report. The results demonstrate some relationships between public opinion and governmental decision-making. These contributions represent a novel advancement in applying generative AI to public governance, bridging the gap between technical sophistication and practical usability in public opinion monitoring.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 20 of its 41 references that are in the map. First posted on arXiv 16 May 2025.

Builds on

20 of 41 references.

References · 20

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8 more
  1. 2020Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learningcited by 15
  2. 2023A Hardware Evaluation Framework for Large Language Model Inferencecited by 14
  3. 2024Towards Hierarchical Multi-Agent Workflows for Zero-Shot Prompt Optimizationcited by 10
  4. 2020Over a Decade of Social Opinion Mining: A Systematic Reviewcited by 7
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2026Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBenchcited by 1
  2. 2025Anemoi: A Semi-Centralized Multi-agent System Based on Agent-to-Agent Communication MCP server from Coral Protocolcited by 0

Similar, not cited

Close in wording, no citation either way

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  3. 2026Reasoning Boosts Opinion Alignment in LLMs
  4. 2023Simulating Opinion Dynamics with Networks of LLM-based Agents
  5. 2026Modeling U.S. Attitudes Toward China via an Event-Steered Multi-Agent Simulator
  6. 2025Measuring Political Preferences in AI Systems: An Integrative Approach
  7. 2024Aligning AI with Public Values: Deliberation and Decision-Making for Governing Multimodal LLMs in Political Video Analysis
  8. 2026LLM Agents Predict Social Media Reactions but Do Not Outperform Text Classifiers: Benchmarking Simulation Accuracy Using 120K+ Personas of 1511 Humans