论文标题
使用倾向得分匹配提高假新闻检测方法的普遍性
Improving Generalizability of Fake News Detection Methods using Propensity Score Matching
论文作者
论文摘要
最近，由于在线社交网络的蓬勃发展，发现假新闻引起了学术社区和公众的重大关注。在本文中，我们考虑假新闻的特征中存在混杂变量，并使用倾向得分匹配（PSM）来选择可概括的功能，以减少混杂变量的影响。实验结果表明，通过使用PSM比使用原始频率选择功能，假新闻方法的普遍性要好得多。我们研究了多种类型的假新闻方法（分类器），例如逻辑回归，随机森林和支持向量机。我们对绩效提高有一致的观察。
Recently, due to the booming influence of online social networks, detecting fake news is drawing significant attention from both academic communities and general public. In this paper, we consider the existence of confounding variables in the features of fake news and use Propensity Score Matching (PSM) to select generalizable features in order to reduce the effects of the confounding variables. Experimental results show that the generalizability of fake news method is significantly better by using PSM than using raw frequency to select features. We investigate multiple types of fake news methods (classifiers) such as logistic regression, random forests, and support vector machines. We have consistent observations of performance improvement.
