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Propagation Structure-Aware Graph Transformer for Robust and Interpretable Fake News Detection

Published: 24 August 2024 Publication History

Abstract

The rise of social media has intensified fake news risks, prompting a growing focus on leveraging graph learning methods such as graph neural networks (GNNs) to understand post-spread patterns of news. However, existing methods often produce less robust and interpretable results as they assume that all information within the propagation graph is relevant to the news item, without adequately eliminating noise from engaged users. Furthermore, they inadequately capture intricate patterns inherent in long-sequence dependencies of news propagation due to their use of shallow GNNs aimed at avoiding the over-smoothing issue, consequently diminishing their overall accuracy. In this paper, we address these issues by proposing the Propagation Structure-aware Graph Transformer (PSGT). Specifically, to filter out noise from users within propagation graphs, PSGT first designs a noise-reduction self-attention mechanism based on the information bottleneck principle, aiming to minimize or completely remove the noise attention links among task-irrelevant users. Moreover, to capture multi-scale propagation structures while considering long-sequence features, we present a novel relational propagation graph as a position encoding for the graph Transformer, enabling the model to capture both propagation depth and distance relationships of users. Extensive experiments demonstrate the effectiveness, interpretability, and robustness of our PSGT.

Supplemental Material

MP4 File - Promotional video for paper 'Propagation Structure-Aware Graph Transformer for Robust and Interpretable Fake News Detection.'
This is the promotional video for our paper titled 'Propagation Structure-Aware Graph Transformer for Robust and Interpretable Fake News Detection.' In the video, we first provide a brief introduction to the background of our fake news detection task. We then briefly explain the core concept of our method, and finally, we present the competitive experimental results achieved by our approach.
MP4 File - Promotional video for paper 'Propagation Structure-Aware Graph Transformer for Robust and Interpretable Fake News Detection.'
This is the promotional video for our paper titled 'Propagation Structure-Aware Graph Transformer for Robust and Interpretable Fake News Detection.' In the video, we first provide a brief introduction to the background of our fake news detection task. We then briefly explain the core concept of our method, and finally, we present the competitive experimental results achieved by our approach.
MP4 File - Promotional video for paper 'Propagation Structure-Aware Graph Transformer for Robust and Interpretable Fake News Detection.'
This is the promotional video for our paper titled 'Propagation Structure-Aware Graph Transformer for Robust and Interpretable Fake News Detection.' In the video, we first provide a brief introduction to the background of our fake news detection task. We then briefly explain the core concept of our method, and finally, we present the competitive experimental results achieved by our approach.

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cover image ACM Conferences
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 2024
6901 pages
ISBN:9798400704901
DOI:10.1145/3637528
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Published: 24 August 2024

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  1. fake news detection
  2. graph transformer
  3. social networks

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  • (2025)Label-aware learning to enhance unsupervised cross-domain rumor detectionJournal of Network and Computer Applications10.1016/j.jnca.2024.104084235(104084)Online publication date: Mar-2025
  • (2025)A review of web infodemic analysis and detection trends across multi-modalities using deep neural networkInternational Journal of Data Science and Analytics10.1007/s41060-025-00727-wOnline publication date: 5-Feb-2025

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