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

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 Multihead Attentive Network to fact-check textual claims. Our model jointly combines multi-head word-level attention and multihead document-level attention, which aid explanation in both word-level and evidencelevel. Experiments on two real-word datasets show that our model outperforms seven stateof-the-art baselines. Improvements over baselines are from 6% to 18%. Our source code and datasets are released at https:// github.com/nguyenvo09/EACL2021.

Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms

“…Algorithmic misinformation detection can be composed of many sub-tasks, which some systems tackle independently while others attempt to solve in an end-to-end fashion. While the specifics of these tasks may evolve and change over time, we draw from Guo et al [34] to differentiate between three core (sequential) tasks: (1) Check-worthiness, which aims to spot factual claims that are worthy of fact-checking [11,31,39,45], (2) Evidence retrieval of potential evidence for identified claims [21,49,56,60,66,70,74] , and (3) verdict prediction, which aims to establish the veracity of a claim [60,63,74]. In a survey on the topic by Zhou and Zafarani [78], the authors identify how misinformation can be detected from four perspectives: (1) the false knowledge it carries; (2) its writing style; (3) its propagation patterns; and (4) the credibility of its source.…”