Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection

• URL: http://arxiv.org/abs/2102.02680v1
• Date: Thu, 4 Feb 2021 15:18:44 GMT
• Title: Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection
• Authors: Nguyen Vo, Kyumin Lee
• Abstract summary: We propose a Hierarchical Multi-head Attentive Network to fact-check textual claims. Our model jointly combines multi-head word-level attention and multi-head document-level attention, which aid explanation in both word-level and evidence-level.
• Score: 11.990139228037124
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The widespread of fake news and misinformation in various domains ranging from politics, economics to public health has posed an urgent need to automatically fact-check information. A recent trend in fake news detection is to utilize evidence from external sources. However, existing evidence-aware fake news detection methods focused on either only word-level attention or evidence-level attention, which may result in suboptimal performance. In this paper, we propose a Hierarchical Multi-head Attentive Network to fact-check textual claims. Our model jointly combines multi-head word-level attention and multi-head document-level attention, which aid explanation in both word-level and evidence-level. Experiments on two real-word datasets show that our model outperforms seven state-of-the-art baselines. Improvements over baselines are from 6\% to 18\%. Our source code and datasets are released at \texttt{\url{https://github.com/nguyenvo09/EACL2021}}.

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