Computational Fact Checking through Query Perturbations
Computational Fact Checking through Query Perturbations
复制标题
通过查询扰动进行计算事实检查
DOI：
10.1145/2996453
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Cong Yu
中科院分区：
文献类型：
--
作者：
You Wu;P. Agarwal;Chengkai Li;Jun Yang;Cong Yu
我们的媒体充斥着从数据中制造出的“事实”的说法。过去，数据库研究的重点是如何回答查询，但没有花太多的精力来辨别由此产生的声明的更微妙的性质，例如，一个声明是“挑剔的”吗？本文提出了一个框架，该框架将基于结构化数据的索赔建模为参数化查询。直观地说，通过对参数设置的选择，声明呈现了底层数据的特定(并且可能存在偏见)视图。一个关键的洞察力是，我们可以通过“扰动”一个主张的参数并观察其结论如何变化来了解它的许多信息。例如，如果对参数的微小扰动可以显著改变其结论，则该声明不是稳健的。这一框架允许我们将实际的事实核查任务--对模糊的索赔进行逆向工程，以及反驳可疑的索赔--作为计算问题。与建模框架一起，我们开发了一个算法框架，它通过提供适当的算法构建块来实现“元”算法的有效实例化。我们提供了真实世界的例子和实验，证明了我们模型的力量，我们算法的效率，以及他们结果的有用性。
Our media is saturated with claims of “facts” made from data. Database research has in the past focused on how to answer queries, but has not devoted much attention to discerning more subtle qualities of the resulting claims, for example, is a claim “cherry-picking”? This article proposes a framework that models claims based on structured data as parameterized queries. Intuitively, with its choice of the parameter setting, a claim presents a particular (and potentially biased) view of the underlying data. A key insight is that we can learn a lot about a claim by “perturbing” its parameters and seeing how its conclusion changes. For example, a claim is not robust if small perturbations to its parameters can change its conclusions significantly. This framework allows us to formulate practical fact-checking tasks—reverse-engineering vague claims, and countering questionable claims—as computational problems. Along with the modeling framework, we develop an algorithmic framework that enables efficient instantiations of “meta” algorithms by supplying appropriate algorithmic building blocks. We present real-world examples and experiments that demonstrate the power of our model, efficiency of our algorithms, and usefulness of their results.