新闻媒体来源的可靠性评估：物以类聚，人以群分
Reliability Estimation of News Media Sources: Birds of a Feather Flock Together
AI总结：
本文提出一种基于强化学习的新闻来源可靠性程度估计方法，通过建模新闻媒体在Web上的交互关系，在更大规模数据集上验证了其与记者评分的高度相关性及对可靠性标签的有效预测能力。
摘要：
评估新闻来源的可靠性是致力于获取和传播准确信息的记者和组织的常规任务。近期研究表明，预测来源的可靠性是应对假新闻检测和事实核查等其他挑战的重要第一步。在本文中，我们提出了一种新的来源可靠性评估方法，利用强化学习策略来估计新闻来源的可靠性程度。与以往研究不同，我们提出的方法基于所有新闻媒体来源在网络上如何相互交互，将该问题建模为可靠性程度的估计，而非可靠性标签的估计。我们在一个新闻媒体可靠性数据集上验证了方法的有效性，该数据集的规模比现有可比数据集大一个数量级。结果表明，估计的可靠性程度与记者提供的评分高度相关（Spearman=0.80），并能有效预测可靠性标签（宏平均 F$_1$ 分数=81.05）。我们公开了实现代码和数据集，旨在为从事信息验证工作的自然语言处理社区提供有价值的资源。
英文摘要：
Evaluating the reliability of news sources is a routine task for journalists and organizations committed to acquiring and disseminating accurate information. Recent research has shown that predicting sources' reliability represents an important first-prior step in addressing additional challenges such as fake news detection and fact-checking. In this paper, we introduce a novel approach for source reliability estimation that leverages reinforcement learning strategies for estimating the reliability degree of news sources. Contrary to previous research, our proposed approach models the problem as the estimation of a reliability degree, and not a reliability label, based on how all the news media sources interact with each other on the Web. We validated the effectiveness of our method on a news media reliability dataset that is an order of magnitude larger than comparable existing datasets. Results show that the estimated reliability degrees strongly correlates with journalists-provided scores (Spearman=0.80) and can effectively predict reliability labels (macro-avg. F$_1$ score=81.05). We release our implementation and dataset, aiming to provide a valuable resource for the NLP community working on information verification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
