{"ID":2878001,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.18819","arxiv_id":"2508.18819","title":"LLM-based Contrastive Self-Supervised AMR Learning with Masked Graph Autoencoders for Fake News Detection","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.","short_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...","url_abs":"https://arxiv.org/abs/2508.18819","url_pdf":"https://arxiv.org/pdf/2508.18819v1","authors":"[\"Shubham Gupta\",\"Shraban Kumar Chatterjee\",\"Suman Kundu\"]","published":"2025-08-26T08:58:35Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.SI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}