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

To detect fake news, researchers proposed to use The proliferation of biased news, misleading linguistics and textual content (Castillo et al., 2011; claims, disinformation and fake news has caused Zhao et al., 2015; Liu et al., 2015). Since textual heightened negative effects on modern society in claims are usually deliberately written to deceive various domains ranging from politics, economics readers, it is hard to detect fake news by solely to public health. A recent study showed that maliciously relying on the content claims. Therefore, multiple fabricated and partisan stories possibly works utilized other signals such as temporal caused citizens' misperception about political candidates spreading patterns (Liu and Wu, 2018), network (Allcott and Gentzkow, 2017) during the structures (Wu and Liu, 2018; Vo and Lee, 2018; 2016 U.S. presidential elections. In economics, the Shu et al., 2020) and users' feedbacks (Vo and spread of fake news has manipulated stock price Lee, 2019; Shu et al., 2019; Vo and Lee, 2020a).