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    <abstract>Social media platforms have become primary sources for news consumption due to their real-time and interactive nature, yet they have also facilitated the widespread proliferation of misinformation, negatively impacting public health, social cohesion, and market stability. While professional fact-checking is essential for debunking rumors, the process is time-consuming, necessitating automation to effectively combat fake news. Existing approaches, such as extractive methods, often lack coherence and context, whereas abstractive methods leveraging large language models (LLMs) can generate more readable and informative debunking passages. However, readability alone is insufficient for effective misinformation correction; user acceptance is critical. Recent advancements in LLMs offer new opportunities for personalized debunking, as these models can generate context-sensitive responses and adapt to user profiles. Building on this, we propose the MUti-round Refinement and Simulated fEedback-enhanced framework (MURSE), which generates Chinese user-specific debunking passages by iteratively refining outputs based on simulated user feedback. Specifically, MURSE-generated user-specific debunking passages were preferred twice as often as general debunking passages in most cases, highlighting its potential to improve misinformation correction and foster positive dissemination chains.</abstract>
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%0 Conference Proceedings
%T Tailoring Rumor Debunking to You: Diversifying Chinese Rumor-Debunking Passages with an LLM-Driven Simulated Feedback-Enhanced Framework
%A Pang, Xinle
%A Wang, Danding
%A Sheng, Qiang
%A Sun, Yifan
%A Hu, Beizhe
%A Cao, Juan
%Y Matusevych, Yevgen
%Y Eryiğit, Gülşen
%Y Aletras, Nikolaos
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-384-5
%F pang-etal-2026-tailoring
%X Social media platforms have become primary sources for news consumption due to their real-time and interactive nature, yet they have also facilitated the widespread proliferation of misinformation, negatively impacting public health, social cohesion, and market stability. While professional fact-checking is essential for debunking rumors, the process is time-consuming, necessitating automation to effectively combat fake news. Existing approaches, such as extractive methods, often lack coherence and context, whereas abstractive methods leveraging large language models (LLMs) can generate more readable and informative debunking passages. However, readability alone is insufficient for effective misinformation correction; user acceptance is critical. Recent advancements in LLMs offer new opportunities for personalized debunking, as these models can generate context-sensitive responses and adapt to user profiles. Building on this, we propose the MUti-round Refinement and Simulated fEedback-enhanced framework (MURSE), which generates Chinese user-specific debunking passages by iteratively refining outputs based on simulated user feedback. Specifically, MURSE-generated user-specific debunking passages were preferred twice as often as general debunking passages in most cases, highlighting its potential to improve misinformation correction and foster positive dissemination chains.
%U https://aclanthology.org/2026.eacl-industry.45/
%P 586-597

Markdown (Informal)

[Tailoring Rumor Debunking to You: Diversifying Chinese Rumor-Debunking Passages with an LLM-Driven Simulated Feedback-Enhanced Framework](https://aclanthology.org/2026.eacl-industry.45/) (Pang et al., EACL 2026)

• Tailoring Rumor Debunking to You: Diversifying Chinese Rumor-Debunking Passages with an LLM-Driven Simulated Feedback-Enhanced Framework (Pang et al., EACL 2026)

ACL

• Xinle Pang, Danding Wang, Qiang Sheng, Yifan Sun, Beizhe Hu, and Juan Cao. 2026. Tailoring Rumor Debunking to You: Diversifying Chinese Rumor-Debunking Passages with an LLM-Driven Simulated Feedback-Enhanced Framework. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track), pages 586–597, Rabat, Morocco. Association for Computational Linguistics.