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[2602.13279] LLM-Enhanced Rumor Detection via Virtual Node Induced Edge Prediction
Summary
This paper presents a novel framework for rumor detection on social networks, utilizing Large Language Models (LLMs) to enhance the identification of subtle rumor signals through virtual node-induced edge prediction.
Why It Matters
As misinformation spreads rapidly on social media, accurate rumor detection is crucial for maintaining information credibility. This research addresses existing limitations in current detection methods by leveraging advanced LLMs, potentially improving the effectiveness of rumor identification and response strategies.
Key Takeaways
- The proposed framework enhances rumor detection by analyzing information subchains with LLMs.
- It introduces a model-agnostic approach, allowing integration with various graph learning algorithms.
- The framework aims to capture complex rumor propagation patterns more effectively.
- A structured prompt framework is developed to mitigate biases in LLMs.
- This research contributes to the ongoing effort to combat misinformation on social networks.
Computer Science > Social and Information Networks arXiv:2602.13279 (cs) [Submitted on 6 Feb 2026] Title:LLM-Enhanced Rumor Detection via Virtual Node Induced Edge Prediction Authors:Jiran Tao, Cheng Wang, Binyan Jiang View a PDF of the paper titled LLM-Enhanced Rumor Detection via Virtual Node Induced Edge Prediction, by Jiran Tao and 2 other authors View PDF HTML (experimental) Abstract:The proliferation of rumors on social networks undermines information credibility. While their dissemination forms complex networks, current detection methods struggle to capture these intricate propagation patterns. Representing each node solely through its textual embeddings neglects the textual coherence across the entire rumor propagation path, which compromises the accuracy of rumor identification on social platforms. We propose a novel framework that leverages Large Language Models (LLMs) to address these limitations. Our approach captures subtle rumor signals by employing LLMs to analyze information subchains, assign rumor probabilities and intelligently construct connections to virtual nodes. This enables the modification of the original graph structure, which is a critical advancement for capturing subtle rumor signals. Given the inherent limitations of LLMs in rumor identification, we develop a structured prompt framework to mitigate model biases and ensure robust graph learning performance. Additionally, the proposed framework is model-agnostic, meaning it is not constrained to...
