Explainable Automated Fact-Checking: A Survey

Proceedings of the 28th International Conference on Computational Linguistics, pages 5430–5443 Barcelona, Spain (Online), December 8-13, 2020 5430 Explainable Automated Fact-Checking: A Survey Neema Kotonya and Francesca Toni Department of Computing Imperial College London, United Kingdom {nk2418,ft}@ic.ac.uk Abstract A number of exciting advances have been made in automated fact-checking thanks to increas- ingly larger datasets and more powerful systems, leading to improvements in the complexity of claims which can be accurately fact-checked. However, despite these advances, there are still desirable functionalities missing from the fact-checking pipeline. In this survey, we focus on the explanation functionality – that is fact-checking systems providing reasons for their predictions. We summarize existing methods for explaining the predictions of fact-checking systems and we explore trends in this topic. Further, we consider what makes for good explanations in this spe- cific domain through a comparative analysis of existing fact-checking explanations against some desirable properties. Finally, we propose further research directions for generating fact-checking explanations, and describe how these may lead to improvements in the research area. 1 Introduction The harms of false claims, which, through their verisimilitude, masquerade as statements of fact, have become widespread as a result of the self-publishing popularized on the modern Web 2.0, without ver- ification. It is now widely accepted that if we are to efficiently respond to this problem, computational approaches are required (Fallis, 2015). Falsehoods take many forms, including audio and graphical content, however a significant quantity of misinformation exists in natural language. For this reason, computational linguistics has a significant role to play in addressing this problem. Steady progress has been made in the area of computational journalism, and more specifically auto- mated fact-checking and its orthogonal tasks. A number of fact-checking tasks have been formulated. Among these tasks are the following: identifying the severity or nature of false claims, i.e., distinguish- ing between hoaxes, rumors and satire (Rubin et al., 2016; Rashkin et al., 2017); detecting check-worthy claims (Hassan et al., 2015; Hassan et al., 2017); distinguishing between hyper-partisan and mainstream news (Potthast et al., 2018); stance detection and opinion analysis in fake news (Hanselowski et al., 2018a); fauxtography, i.e., verifying the captions or claims which accompany published images (Zlatkova et al., 2019); table-based fact verification (Chen et al., 2020); credibility assessment (Mitra and Gilbert, 2015; Derczynski et al., 2017); and an end-to-end pipeline which encompasses both the document-level evidence retrieval and sentence-level evidence selection sub-tasks of claim verification (Thorne et al., 2018). Established orthogonal tasks such as claim detection (Levy et al., 2014; Lippi and Torroni, 2015) and deception detection (Conroy et al., 2015) also play a key role in automated fact-checking. The fact-checking pipeline is shown in Figure 1. The focus of this survey is explainable automated fact-checking. Indeed, over the last few years strong progress has been made both in terms of the performance of deep learning models for fact-checking and also in terms of the availability of comprehensive datasets for training these models. However, not nearly as much work has been devoted to …

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