Development of methods to detect fake news (FN) in low-resource languages has
been impeded by a lack of training data. In this study, we solve the problem by
using only training data from a high-resource language. Our FN-detection system
permitted this strategy by applying adversarial learning that transfers the
detection knowledge through languages. To assist the knowledge transfer, our
system judges the reliability of articles by exploiting source information,
which is a cross-lingual feature that represents the credibility of the
speaker. In experiments, our system got 3.71% higher accuracy than a system
that uses a machine-translated training dataset. In addition, our suggested
cross-lingual feature exploitation for fake news detection improved accuracy by
3.03%.

本研究提出了一种利用高资源语言训练数据来检测低资源语言中的假新闻的方法，采用对抗学习技术来实现跨语言的检测知识转移，同时利用源信息提高文章可靠性并通过跨语言特征来提高假新闻检测系统的准确性。经实验证明，此方法可以使假新闻检测系统比使用机器翻译训练数据的系统提高 3.71% 的准确率，并将跨语言特征的应用提高了 3.03% 的准确率。