论文标题
所有好演员看起来都一样吗？探索美国和英国的新闻真实检测
Do All Good Actors Look The Same? Exploring News Veracity Detection Across The U.S. and The U.K
论文作者
论文摘要
基于文本的新闻真实检测方法的主要关注点是，它们可能不会跨越国家和文化。在这篇简短的论文中，我们明确测试了来自美国和英国的新闻数据的新闻真实模型，这表明有理由关注Glenerizabilty。通过一系列的测试方案，我们表明，在一个国家的新闻数据接受培训并对另一个国家进行测试时，基于文本的分类器的性能很差。此外，这些相同的模型难以对看不见的，不可靠的新闻来源进行分类。总之，我们讨论了这些结果的含义以及对未来工作的途径。
A major concern with text-based news veracity detection methods is that they may not generalize across countries and cultures. In this short paper, we explicitly test news veracity models across news data from the United States and the United Kingdom, demonstrating there is reason for concern of generalizabilty. Through a series of testing scenarios, we show that text-based classifiers perform poorly when trained on one country's news data and tested on another. Furthermore, these same models have trouble classifying unseen, unreliable news sources. In conclusion, we discuss implications of these results and avenues for future work.
