Exploring Large Language Models for Effective Rumor Detection on Social Media (2025)

Abstract: In this paper, we explore using Large Language Models (LLMs) for rumor detection on social media. It involves assessing the veracity of claims on social media based on social context (e.g., comments, propagation patterns). LLMs, despite their impressive capabilities in text-based reasoning tasks, struggle to achieve promising rumor detection performance when facing long structured social contexts. Our preliminary analysis shows that large-scale contexts hinder LLMs’ reasoning abilities, while moderate contexts perform better for LLMs, highlighting the need for refined contexts. Accordingly, we propose a semantic-propagation collaboration-base framework that integrates small language models (e.g., graph attention network) with LLMs for effective rumor detection. It models contexts by enabling text semantic and propagation patterns to collaborate through graph attention mechanisms, and reconstruct the context by aggregating attention values during inference. Also, a cluster-based unsupervised method to refine context is proposed for generalization. Extensive experiments demonstrate the effectiveness of proposed methods in rumor detection. This work bridges the gap for LLMs in facing long, structured data and offers a novel solution for rumor detection on social media.

## introduction the rise of social media has made the dissemination of information more convenient and widespread. however, it has also made the issue of rumor become dominant. more seriously, the malicious use of llms facilitates rumor creation and may bring larger risks in the near future @xcite @xcite . although human countermeasures like establishing reporting mechanisms and conducting fact-checking have been adopted @xcite , their inevitable lag effect makes rumors costly and difficult to verify in real-time. therefore, automatic rumor detection on social media is highly desirable and socially beneficial. most traditional works for detecting rumors focus on incorporating various information (e.g., credibility @xcite , social context @xcite , extra knowledge @xcite ) to learn the latent features of rumors via fine-tuned small language models (slms) (e.g., deep neural networks @xcite @xcite ). among them, models based on social context and graph neural networks have been widely proposed and achieved sota performances @xcite . however, their performance is limited by real-world requirements (e.g., explainability, zero-shot capabilities). therefore, rumor detection with slms still seeks better solutions in real-world scenarios. in rumor detection systems with llms, most studies treat llms as secondary components (e.g., context simulator @xcite , advisors @xcite for slms). they have made progress in detection precision, but their application in real-world scenarios, which require explainable or zero-shot capabilities, remains limited. some works @xcite attempt to apply llms as the primary decision maker in de-tecting rumors to overcome these limitations. unfortunately, the experiments reveal a gap between llms and fine-tuned slms. it can be attributed to the following reasons and challenges: (1) social contexts usually contain large amounts of text comments. llms struggle to focus on key clues and get lost in the middle when faced with long texts or redundant information @xcite . (2) the propagation patterns within social contexts are critical for rumor detection, however, llms lack proficiency in handling such structured data @xcite . tackling the above challenges requires us to investigate efficient ways of modeling social context for llms, which includes both extensive text comments and structured propagation patterns on social media. to this end, we conduct a preliminary analysis of social contexts( §3). the results demonstrate that llms perform better when reasoning over moderate-sized social contexts, rather than those that are too long or too short. additionally, tightlyknit groups are often formed based on shared user interests in the social context, and the key nodes within these groups could provide important information. this insight suggests a solution to these challenges: refining the social context for llms by focusing on the key nodes within it. the comparison of our method with existing approaches that use llms as primary decision-makers is demonstrated in figure 1 . based on the above insights, we propose a semantic-propagation collaboration-based rumor detection framework (sepro), which refines the social context for llms. its strength lies in integrating llms with graph neural networks to enhance the system's ability to process large-scale structured data. specifically, we first construct propagation and semantic graphs based on social context. then, we employ supervised graph attention networks (gats) to model propagation patterns and text semantic features. simultaneously, by extracting attention values from gats, we enable collaboration between these features to identify core nodes, and reconstruct the social context into moderate size. additionally, we propose a cluster-based unsupervised method to refine the social context for generalization. finally, based on refined social context, we design an elaborate chain-of-clue prompt to verify the claim and generate explanations by inferring reasons on key clues. extensive experiments demonstrate that our proposed methods have achieved excellent performance in detecting rumors and providing explanations. ## social context analysis social context refers to the comments, replies and propagation patterns linked to a claim on social networks. it sheds light on information spread, user reactions, and interaction dynamics around the claim. firstly, for llms on large-scale social context analysis, recent researches reveal that llms cannot reason well over redundant information @xcite . to investigate this limitation in rumor detection, we tested the performance of widely-used llms (gpt-3.5-turbo-0125 (ope-nai, 2024)) with a vanilla prompt on two datasets, i.e., twitter @xcite and weibo @xcite (see secondly, to conveniently refine the large-scale social context to a moderate size, we analyse its clustering phenomenon. in social networks, individuals form tightly-knit groups, known as community clusters @xcite , due to similar user preferences @xcite and relationships. they are characterized by dense internal connections and relatively sparse connections with the rest of the network, with each community guided by a few central nodes, typically acting as hubs. to further investigate this, we analyzed user preferences to engage in rumor propagation based on interaction records from above datasets. we assign a value to interactions based on the claim label: 0 for fake (rumor, false), 0.5 for neutral (unverified), and 1 for real (non-rumor, true). each user receives an interaction value corresponding to the claim they engage with. we then calculate each user's average interaction value, denoted as p (user preference). users are grouped by p and their proportions (%) are reported in figure 3 . to present the results more smoothly, we applied log normalization. we observed three notable peaks at the low (fake), high (real), and middle (neutral) user preference values. this reveals that during rumor propagation, users with similar preferences tend to cluster together. the above analysis result suggests to model the entire large-scale social context, then reconstruct it based on core nodes to a moderate size suitable for llms. additionally, individuals often base their decisions on others' actions rather than their own information @xcite . this can trigger a chain reaction of similar decisions, resulting in an information cascade. it suggests the context that directly interacts with claim (i.e., local context) in a network have the greatest influence. based on this idea, we propose a framework to models social context from both local and global (entire social context) perspectives. ## methods this section begins with a formal definition of rumor detection. then, we present our rumor detection framework sepro in figure 4 . given the input sample including claim and its social context (text comments, propagation structure), we first construct a propagation graph and a semantic graph. after that, we use a global semantic-propagation collaboration module to capture global semantic and propagation features, enabling features collaboration to refine the global social context. a local aggregation module proposed to refine local context. finally, proposed a chain-of-clue prompt for better reasoning over claims and social context. ## conclusion in this paper, we investigate using llms for effective rumor detection. currently, llms have challenges in modeling large-scale structured data. our analysis reveals moderate context is crucial for llms, suggesting to refine long structured contexts. we propose a rumor detection framework, which integrates graph attention neural networks with llms to model and reconstruct contexts from multiple perspectives. it fills the gap in utilizing llms for rumor detection on social media.