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Open Access

ARTICLE

OPOR-Bench: Evaluating Large Language Models on Online Public Opinion Report Generation

Jinzheng Yu1, Yang Xu2, Haozhen Li2, Junqi Li3, Ligu Zhu1, Hao Shen1,*, Lei Shi1,*
1 State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, 100024, China
2 Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology, Harbin, 150001, China
3 Scientific and Information Technical Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing, 100081, China
* Corresponding Author: Hao Shen. Email: email; Lei Shi. Email: email
(This article belongs to the Special Issue: Big Data and Artificial Intelligence in Control and Information System)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.073771

Received 25 September 2025; Accepted 24 November 2025; Published online 19 December 2025

Abstract

Online Public Opinion Reports consolidate news and social media for timely crisis management by governments and enterprises. While large language models (LLMs) enable automated report generation, this specific domain lacks formal task definitions and corresponding benchmarks. To bridge this gap, we define the Automated Online Public Opinion Report Generation (OPOR-Gen) task and construct OPOR-Bench, an event-centric dataset with 463 crisis events across 108 countries (comprising 8.8 K news articles and 185 K tweets). To evaluate report quality, we propose OPOR-Eval, a novel agent-based framework that simulates human expert evaluation. Validation experiments show OPOR-Eval achieves a high Spearman’s correlation (ρ = 0.70) with human judgments, though challenges in temporal reasoning persist. This work establishes an initial foundation for advancing automated public opinion reporting research.

Keywords

Online public opinion reports; crisis management; large language models; agent-based evaluation

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