论文标题
自动索赔检测模型中的CHECKWORNANIONS：数据集的定义和分析
Checkworthiness in Automatic Claim Detection Models: Definitions and Analysis of Datasets
论文作者
论文摘要
在过去的十年中，公众，专业和学术兴趣已大大增加，许多人旨在自动化事实检查程序的第一步之一：选择所谓的Checkworthy索赔。但是，关于事实检查器中核对道值的定义和特征几乎没有共识，因此，这反映在用于培训和测试Checkworthy索赔检测模型的数据集中。在对事实检查组织中的CheckWorthy索赔选择程序进行了详细的分析以及对最先进的索赔检测数据集的分析之后，Checkwornesilence被定义为具有时空和上下文依赖性价值的概念，并且需要具有其验证的客观性的正确性。这是基于先验知识和信念的个人对个人的真实性判断的看法。关于当前数据集的特征，人们认为数据不仅高度失衡和嘈杂，而且范围和语言也太有限。此外，我们认为Checkworniss的主观概念可能不是索赔检测的合适过滤器。
Public, professional and academic interest in automated fact-checking has drastically increased over the past decade, with many aiming to automate one of the first steps in a fact-check procedure: the selection of so-called checkworthy claims. However, there is little agreement on the definition and characteristics of checkworthiness among fact-checkers, which is consequently reflected in the datasets used for training and testing checkworthy claim detection models. After elaborate analysis of checkworthy claim selection procedures in fact-check organisations and analysis of state-of-the-art claim detection datasets, checkworthiness is defined as the concept of having a spatiotemporal and context-dependent worth and need to have the correctness of the objectivity it conveys verified. This is irrespective of the claim's perceived veracity judgement by an individual based on prior knowledge and beliefs. Concerning the characteristics of current datasets, it is argued that the data is not only highly imbalanced and noisy, but also too limited in scope and language. Furthermore, we believe that the subjective concept of checkworthiness might not be a suitable filter for claim detection.
