Structure-aware Propagation Generation with Large Language Models for Fake News Detection (2025)

Abstract: The spread of fake news on social media poses a serious threat to public trust and societal stability. While propagation-based methods improve fake news detection by modeling how information spreads, they often suffer from incomplete propagation data. Recent work leverages large language models (LLMs) to generate synthetic propagation, but typically overlooks the structural patterns of real-world discussions. In this paper, we propose a novel structure-aware synthetic propagation enhanced detection (StruSP) framework to fully capture structural dynamics from real propagation. It enables LLMs to generate realistic and structurally consistent propagation for better detection. StruSP explicitly aligns synthetic propagation with real-world propagation in both semantic and structural dimensions. Besides, we also design a new bidirectional evolutionary propagation (BEP) learning strategy to better align LLMs with structural patterns of propagation in the real world via structure-aware hybrid sampling and masked propagation modeling objective. Experiments on three public datasets demonstrate that StruSP significantly improves fake news detection performance in various practical detection scenarios. Further analysis indicates that BEP enables the LLM to generate more realistic and diverse propagation semantically and structurally.

## introduction the rapid advancement of online media has led to an alarming rise in fake news, posing significant threats to public trust and societal stability @xcite @xcite . existing methods of fake news detection mainly focus on textual content such as news text and contexts @xcite @xcite , and propagation information such as interactions between users (lu and te li, 2020; su et al., * corresponding author. @xcite ) and original propagation of the news "donald trump has been disqualified from running for president. " 2023; @xcite @xcite @xcite . despite their promise, propagationbased methods often suffer in incomplete propagation scenarios due to limited data collection and some malicious user interactions on social media @xcite . recently, llms have shown potential in alleviating data scarcity by simulating user-generated propagation through role-playing approaches @xcite @xcite @xcite . however, these llm-enhanced methods typically operate only at the semantic level, neglecting the structural patterns of real-world propagation. as shown in figure 1 , the generated propagation trees often exhibit overly uniform structures due to the predefined branch probability @xcite and prompt-induced alignment behavior @xcite , failing to capture the irregular branching and hierarchical depth that characterize real-world information spread @xcite . in addition, their generated content often lacks emotional diversity and context sensitivity, tending toward overly cautious or generic tones @xcite . the mismatch between the generated and real propagation structures and semantics significantly limits their effectiveness in downstream detection, particularly in early detection and crossplatform generalization. this highlights the need for a structure-aware generation framework that captures both the semantic plausibility and structural dynamics of real-world propagation. to address the above limitation, we propose a novel structure-aware synthetic propagation enhanced detection (strusp) framework to fully capture sufficient features from real-world and llmgenerated synthetic propagation structure. strusp enriches incomplete propagation trees by generating realistic and structurally consistent propagation paths, thereby improving the effectiveness of propagation-based fake news detection. to ensure the generated propagation reflects real-world structural dynamics, we introduce a bidirectional evolutionary propagation (bep) learning strategy to align llms with structural patterns of propagation in the real world. bep consists of two main components. the structure-aware hybrid sampling module first samples propagation substructures via both breadth-wise and depth-wise progression of available propagation trees. based on these sampled paths, the masked propagation modeling objective captures structural dependencies by reconstructing masked nodes in both forward and backward directions. this design enables the llm to effectively learn structural evolution patterns in realworld propagation, equipping it with the ability to enrich incomplete propagation through a structure propagation enhancement module for more accurate detection. experiments on three real-world datasets demonstrate that strusp not only improves detection performance under incomplete propagation conditions but also generates propagation patterns that closely match real data, consistently surpassing baseline methods across structural and semantic metrics. the contributions of this work can be summarized as follows: content-based fake news detection methods extract semantic patterns from news content for detection through feature engineering @xcite @xcite and a wide array of deep learning architectures, including neural networks @xcite and pre-trained language models @xcite . some works also integrate tasks such as stance detection and sentiment analysis with fake news detection, enabling multi-task learning @xcite . since some fake news creators imitate the style of real news, methods based solely on news content often face limitations. consequently, some researchers use news comments as a basis for assessing the authenticity of news @xcite . propagation-based fake news detection methods capture the propagation patterns of news by modeling the interactions between news and comments into time series @xcite or topological structures such as propagation trees @xcite and propagation graphs @xcite @xcite . some studies further explore multi-relational interactions between the users and news in the propagation graph @xcite . however, these methods suffer significant performance losses when confronted with scenarios of incomplete propagation @xcite . llm-based propagation generation llms have been proven to have the potential to simulate human behavior @xcite and possess a certain level of social knowledge @xcite . ## strusp framework problem statement fake news detection is to verify the authenticity of a given news article, we take it as a binary classification problem, where each sample is annotated with a ground truth label indicating its authenticity. formally, dataset d consists of n samples and each sample is represented by @xmath0, e), where @xmath1, c 1 , ..., c n } represents the news n and its comments (c 1 , ..., c n ), e represents a set of explicit interactive behaviors (e.g., retweet). the task objective of fake news detection is to learn a classifier f to classify samples and determine whether the news is true (labeled as 0) or false (labeled as 1), i.e., f : g -→ y, @xmath2, 1}. ## experimental results we evaluate the effectiveness of strusp on three real-world fake news datasets across different detection scenarios (section 5.1) and conduct ablation studies to evaluate the effectiveness of each component in strusp (section 5.2). we further analyze the structural differences between synthetic and real propagation at two levels (section 5.3): a macro-level analysis, which compares average metric values across samples, and a micro-level analysis, which examines their distribution at the individual sample level. we replace the llm backbone of strusp to investigate the impact of llm choice on the framework's performance (section 5.4). ## conclusion this paper proposes a structure-aware synthetic propagation enhanced fake news detection framework (strusp). by employing a bidirectional evolutionary propagation learning strategy, strusp enables llms to generate realistic and informative propagation trees and enrich the existing incomplete propagation tree. experiments on three datasets demonstrate that strusp significantly improves fake news detection performance in different incomplete propagation settings and produces realistic and diverse propagation. semantic homogeneity (semh) measures pairwise coherence among all comments in a propagation, the cosine similarity is used to calculate the semantic homogeneity: our framework achieves robustness against prompt variations through two core design principles: these design choices collectively ensure that our method's performance is robust and reliable across different prompt formulations.