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

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, pages 965–975 April 19 - 23, 2021. ©2021 Association for Computational Linguistics 965 Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection Nguyen Vo Worcester Polytechnic Institute Computer Science Department Worcester, MA, USA, 01609 nkvo@wpi.edu Kyumin Lee Worcester Polytechnic Institute Computer Science Department Worcester, MA, USA, 01609 kmlee@wpi.edu Abstract The widespread of fake news and misinforma- tion 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 uti- lize 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 tex- tual claims. Our model jointly combines multi-head word-level attention and multi- head document-level attention, which aid ex- planation 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 base- lines are from 6% to 18%. Our source code and datasets are released at https:// github.com/nguyenvo09/EACL2021. 1 Introduction The proliferation of biased news, misleading claims, disinformation and fake news has caused heightened negative effects on modern society in various domains ranging from politics, economics to public health. A recent study showed that ma- liciously fabricated and partisan stories possibly caused citizens’ misperception about political can- didates (Allcott and Gentzkow, 2017) during the 2016 U.S. presidential elections. In economics, the spread of fake news has manipulated stock price (Kogan et al., 2019). For example, $139 billion was wiped out when the Associated Press (AP)’s hacked Twitter account posted rumor about White House explosion with Barack Obama’s injury. Re- cently, misinformation has caused infodemics in public health (Ashoka, 2020) and even led to peo- ple’s fatalities in the physical world (Alluri, 2019). To reduce the spread of misinformation and its detrimental influences, many fact-checking sys- tems have been developed to fact-check textual claims. It is estimated that the number of fact- checking outlets has increased 400% in 60 coun- tries since 2014 (Stencel, 2019). Several fact- checking systems such as snopes.com and politi- fact.com are widely used by both online users and major corporations. Facebook (CNN, 2020) re- cently incorporated third-party fact-checking sites to social media posts and Google integrated fact- checking articles to their search engine (Wang et al., 2018). These fact-checking systems debunk claims by manually assess their credibility based on col- lected webpages used as evidence. However, this manual process is laborious and unscalable to han- dle the large volume of produced false claims on communication platforms. Therefore, in this pa- per, our goal is to build an automatic fake news detection system to fact-check textual claims based on collected evidence to speed up fact-checking process of the above fact-checking sites. To detect fake news, researchers proposed to use linguistics and textual content (Castillo et al., …

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