LLM-based Contrastive Self-Supervised AMR Learning with Masked Graph Autoencoders for Fake News Detection

• URL: http://arxiv.org/abs/2508.18819v1
• Date: Tue, 26 Aug 2025 08:58:35 GMT
• Title: LLM-based Contrastive Self-Supervised AMR Learning with Masked Graph Autoencoders for Fake News Detection
• Authors: Shubham Gupta, Shraban Kumar Chatterjee, Suman Kundu,
• Abstract summary: The proliferation of misinformation in the digital age has led to significant societal challenges.<n>Existing approaches often struggle with capturing long-range dependencies, complex semantic relations, and the social dynamics influencing news dissemination.<n>We propose a novel self-supervised misinformation detection framework that integrates both complex semantic relations and news propagation dynamics.
• Score: 17.045049022252563
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The proliferation of misinformation in the digital age has led to significant societal challenges. Existing approaches often struggle with capturing long-range dependencies, complex semantic relations, and the social dynamics influencing news dissemination. Furthermore, these methods require extensive labelled datasets, making their deployment resource-intensive. In this study, we propose a novel self-supervised misinformation detection framework that integrates both complex semantic relations using Abstract Meaning Representation (AMR) and news propagation dynamics. We introduce an LLM-based graph contrastive loss (LGCL) that utilizes negative anchor points generated by a Large Language Model (LLM) to enhance feature separability in a zero-shot manner. To incorporate social context, we employ a multi view graph masked autoencoder, which learns news propagation features from social context graph. By combining these semantic and propagation-based features, our approach effectively differentiates between fake and real news in a self-supervised manner. Extensive experiments demonstrate that our self-supervised framework achieves superior performance compared to other state-of-the-art methodologies, even with limited labelled datasets while improving generalizability.

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