@inproceedings{yang-etal-2025-rethink,
title = "Rethink Rumor Detection in the Era of {LLM}s: A Review",
author = "Yang, Chang and
Zhang, Peng and
Zhang, Jing and
Gao, Hui and
Song, Changhao",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.464/",
pages = "8730--8749",
ISBN = "979-8-89176-335-7",
abstract = "The rise of large language models (LLMs) has fundamentally reshaped the technological paradigm of rumor detection, offering transformative opportunities to construct adaptive detection systems while simultaneously ushering in new threats, such as ``logically perfect rumors''. This paper aims to unify existing methods in the field of rumor detection and reveal the logical mechanisms behind them. From the perspective of complex systems, we innovatively propose a Cognition-Interaction-Behavior (CIB) tri-level framework for rumor detection based on collective intelligence and explore the synergistic relationship between LLMs and collective intelligence in rumor governance. We identify promising future research directions, including advancing agent-based modeling to capture complex rumor dynamics, addressing emerging challenges unique to the LLM era, and interdisciplinary perspectives. We hope this work lays a theoretical foundation for next-generation rumor detection paradigms and offers valuable insights for advancing the field."
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<abstract>The rise of large language models (LLMs) has fundamentally reshaped the technological paradigm of rumor detection, offering transformative opportunities to construct adaptive detection systems while simultaneously ushering in new threats, such as “logically perfect rumors”. This paper aims to unify existing methods in the field of rumor detection and reveal the logical mechanisms behind them. From the perspective of complex systems, we innovatively propose a Cognition-Interaction-Behavior (CIB) tri-level framework for rumor detection based on collective intelligence and explore the synergistic relationship between LLMs and collective intelligence in rumor governance. We identify promising future research directions, including advancing agent-based modeling to capture complex rumor dynamics, addressing emerging challenges unique to the LLM era, and interdisciplinary perspectives. We hope this work lays a theoretical foundation for next-generation rumor detection paradigms and offers valuable insights for advancing the field.</abstract>
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%0 Conference Proceedings
%T Rethink Rumor Detection in the Era of LLMs: A Review
%A Yang, Chang
%A Zhang, Peng
%A Zhang, Jing
%A Gao, Hui
%A Song, Changhao
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F yang-etal-2025-rethink
%X The rise of large language models (LLMs) has fundamentally reshaped the technological paradigm of rumor detection, offering transformative opportunities to construct adaptive detection systems while simultaneously ushering in new threats, such as “logically perfect rumors”. This paper aims to unify existing methods in the field of rumor detection and reveal the logical mechanisms behind them. From the perspective of complex systems, we innovatively propose a Cognition-Interaction-Behavior (CIB) tri-level framework for rumor detection based on collective intelligence and explore the synergistic relationship between LLMs and collective intelligence in rumor governance. We identify promising future research directions, including advancing agent-based modeling to capture complex rumor dynamics, addressing emerging challenges unique to the LLM era, and interdisciplinary perspectives. We hope this work lays a theoretical foundation for next-generation rumor detection paradigms and offers valuable insights for advancing the field.
%U https://aclanthology.org/2025.findings-emnlp.464/
%P 8730-8749
Markdown (Informal)
[Rethink Rumor Detection in the Era of LLMs: A Review](https://aclanthology.org/2025.findings-emnlp.464/) (Yang et al., Findings 2025)
ACL
- Chang Yang, Peng Zhang, Jing Zhang, Hui Gao, and Changhao Song. 2025. Rethink Rumor Detection in the Era of LLMs: A Review. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 8730–8749, Suzhou, China. Association for Computational Linguistics.