Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection
This work provides an incremental improvement in fake news detection for general users and platforms by offering a more effective method for fact-checking textual claims.
This paper addresses the problem of fake news detection by proposing a Hierarchical Multi-head Attentive Network. The model combines multi-head word-level and document-level attention, outperforming seven state-of-the-art baselines with improvements ranging from 6% to 18% on two real-world datasets.
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}}.