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GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media

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arxiv 2004.11648 v1 pith:VEDJ2BF6 submitted 2020-04-24 cs.CL cs.LGstat.ML

Yi-Ju Lu , Cheng-Te Li This is my paper · ORCID
classification cs.CLcs.LGstat.ML
keywords gcanfaketweetco-attentiondetectiongraph-awaremedianetworks
verification ladder T0 review T1 audit T2 compute T3 formal

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This paper solves the fake news detection problem under a more realistic scenario on social media. Given the source short-text tweet and the corresponding sequence of retweet users without text comments, we aim at predicting whether the source tweet is fake or not, and generating explanation by highlighting the evidences on suspicious retweeters and the words they concern. We develop a novel neural network-based model, Graph-aware Co-Attention Networks (GCAN), to achieve the goal. Extensive experiments conducted on real tweet datasets exhibit that GCAN can significantly outperform state-of-the-art methods by 16% in accuracy on average. In addition, the case studies also show that GCAN can produce reasonable explanations.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MPPFND: A Dataset and Analysis of Detecting Fake News with Multi-Platform Propagation

    cs.SI 2025-05 conditional novelty 6.0 of 10

    Using a new three-platform propagation dataset, a graph-based model that adapts to each platform's patterns outperforms single-platform and content-only detectors for fake news.

  2. REFLEX: Self-Refining Explainable Fact-Checking via Verdict-Anchored Style Control

    cs.CL 2025-11 unverdicted novelty 5.0 of 10

    REFLEX improves explainable fact-checking by using verdict-anchored style control and self-disagreement signals to disentangle fact from style in LLM outputs, achieving SOTA results with minimal self-refined samples.

  3. Multi-view Fake News Detection Model Based on Dynamic Hypergraph

    cs.LG 2024-12 conditional novelty 5.0 of 10

    DHy-MFND learns news embeddings from text, propagation trees, and a dynamically refined hypergraph, and reports state-of-the-art accuracy on PolitiFact and Gossipcop.

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