Relevant Document Discovery for Fact-Checking Articles

被引:28
作者
Wang, Xuezhi [1 ]
Yu, Cong [1 ]
Baumgartner, Simon [1 ]
Korn, Flip [1 ]
机构
[1] Google Res NYC, Cambridge, MA 02138 USA
来源
COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018) | 2018年
关键词
Fact Checking; Digital Misinformation; Claim-Relevance Discovery;
D O I
10.1145/3184558.3188723
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the support of major search platforms such as Go ogle and Bing, fact-checking articles, which can be identified by their adoption of the schema.org ClaimReview structured markup, have gained widespread recognition for their role in the fight against digital misinformation. A claim-relevant document is an online document that addresses, and potentially expresses a stance towards, some claim. The claim-relevance discovery problem, then, is to find claim-relevant documents. Depending on the verdict from the fact check, claim-relevance discovery can help identify online misinformation. In this paper, we provide an initial approach to the claim-relevance discovery problem by leveraging various information retrieval and machine learning techniques. The system consists of three phases. First, we retrieve candidate documents based on various features in the fact-checking article. Second, we apply a relevance classifier to filter away documents that do not address the claim. Third, we apply a language feature based classifier to distinguish documents with different stances towards the claim. We experimentally demonstrate that our solution achieves solid results on a large-scale dataset and beats state-of-the-art baselines. Finally, we highlight a rich set of case studies to demonstrate the myriad of remaining challenges and that this problem is far from being solved.
引用
收藏
页码:525 / 533
页数:9
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