Rethink Rumor Detection in the Era of LLM s: A Review (2025)

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.

## introduction in the digital era, the widespread adoption of social media and the explosion of user-generated content have enabled rumors to threaten public safety and social trust at unprecedented speeds, scales, and levels of complexity @xcite . meanwhile, the rapid advancements in large language models (llms) have demonstrated remarkable performance across various fields @xcite , but it has also brought challenges that cannot be ignored. models like gpt-4 @xcite and deepseek @xcite , known for their deep semantic understanding and reasoning capabilities, can generate highly credible and logically coherent professional content. however, this ability can also be used to generate "logically perfect rumors" (such as false arguments based on chain reasoning), which are far more concealed and misleading than traditional generation methods. @xcite . for example, studies have shown that chatgpt, when provided with malicious prompts, can not only optimize deceptive text but also proactively enhance their disguise by incorporating additional misleading details @xcite . thus, leveraging the powerful capabilities of llms while addressing their inherent limitations has emerged as an urgent challenge in the field of rumor detection. existing rumor detection surveys primarily focus on the dissemination mechanisms of rumors on social media @xcite @xcite , the psychological mechanisms underlying belief in rumors @xcite , and effective intervention strategies @xcite . however, most existing frameworks primarily rely on feature-based or technical classifications, which result in two primary issues: (1) the failure to thoroughly explore the theoretical and logical connections between detection methods and rumor propagation mechanisms, and (2) the inability to effectively reveal the intrinsic relationships between features, especially in the context of research on llms in this field @xcite . to bridge this gap, we introduce a cognition-interaction-behavior (cib) tri-level framework to systematically elucidate the underlying logic of rumor propagation and detection on social networks. the specific contributions of this work include: (1) a new theoretical paradigm for rumor detection. the construction of the cib framework unifies existing rumor detection methods and uncovers the multi-scale coupling mechanisms underlying rumor propagation, including collective knowledge emergence, interactive network evolution, and iterative behavioral patterns. (2) a systematic ex-figure 1 : the three-layer architecture operates collaboratively. the cognition layer integrates multi-source evidence to provide informational support for the interaction layer. through user interactions, the interaction layer facilitates the formation of the behavior layer. the behavior layer, in turn, continuously refines the cognition layer through accumulated experiences and collective cognitive feedback. (rd is rumor detection; ci is collective intelligence, driving information's dynamic reconstruction and optimization). ## collective intelligence-based rumor detection framework in the social media ecosystem, user communities serve dual roles as both disseminators and evaluators, forming the self-organizing foundation of the networked information ecology. studies have shown that through cross-validation among users and the interplay of opinions, social networks can facilitate collective cognitive correction @xcite . compared to individual cognition, collective intelligence leverages the integration of diverse knowledge and dynamic interactions, demonstrating superior cognitive capabilities in addressing complex information @xcite , thereby offering a novel approach to advancing rumor @xcite . from the perspective of complex systems, the emergence of collective intelligence is essentially a self-organizing process driven by the reduction of information entropy. during this process, social media users' diverse cognition, social connections, and dynamic behaviors interact, facilitating information flow and collaborative evolution. rumor diffusion, as a specific form of information dissemination, is often constrained by individuals' cognitive thresholds (e.g., cognitive abilities, emotional biases) and the topological structure of the social network. at its core, rumor diffusion can be viewed as a staged state of cognitive imbalance: it arises when users, driven by information uncertainty and emotional impetus, engage in social interactions to reduce uncertainty, which in turn drives the continuous evolution of network structures @xcite . this process generates macro-level dissemination behaviors (potentially unintentionally promoting rumor propagation). however, collective intelligence can dynamically correct such imbalanced states in social networks through multi-level knowledge sharing and interaction. based on the above theoretical construction, this study proposes a tri-level framework for rumor detection based on collective intelligence, as shown in figure 1 . the cognition layer facilitates the construction of crowd knowledge for rumor identification through knowledge sharing and evidence integration among users. it serves as the foundational support and aggregates multidimensional evidence. the interaction layer analyzes users' social relationships and interaction behaviors within social networks to capture rumor signals. the behavior layer models the evolution of information dissemination and collective behavior. finally, the feedback mechanism based on collective intelligence optimizes the dissemination path and reduces the spread of rumors. ## collective intelligence-based rumor detection in the llm era in this chapter, we explore the synergistic relationship between llm and collective intelligence in rumor detection, that is, how llm enhances the cib framework to play various important roles in rumor detection, while the collective intelligence mechanism drives llm agent-based modeling to enhance the simulation capabilities of rumor detection. we present an agent-based modeling approach within the cib framework and establish a macro-micro feedback loop that can link cognitive processes and behavioral outcomes in rumor detection. ## future research we further explore two important frontiers in this chapter: advancing agent-based modeling to capture complex rumor dynamics, and addressing emerging challenges unique to the llm era, combining the cib framework to give the future direction of rumor detection, and we also provide more interdisciplinary perspectives in appendix b. ## conclusion based on the complex system characteristics of collective intelligence, we have reconstructed a rumor detection paradigm-the cognition-interaction-behavior (cib) framework-adapted to the era of llms. we thoroughly explored the multidimensional roles of llms in enhancing rumor detection capabilities and their synergistic relationship with collective intelligence. innovatively, the cib framework enables dynamic bidirectional rumor detection and intervention, providing a roadmap for applying agent-based modeling (abm) in rumor detection. we analyzed the emerging challenges in the llm era and proposed feasible future research directions, providing theoretical foundations and developmental pathways for rumor detection.