论文标题
虚假新闻的预测语言提示：社会人工智能问题
Predictive linguistic cues for fake news: a societal artificial intelligence problem
论文作者
论文摘要
媒体新闻是公众舆论的很大一部分，因此绝不是假的。在发布之前，必须对网站，博客和社交媒体进行分析的新闻。在本文中，我们介绍了媒体新闻项目的语言特征，以使用机器学习算法区分虚假新闻和真实新闻。神经假新闻，机器创建的头条新闻，机器产生的文本和图像标题的语义不一致是其他类型的假新闻问题。这些问题使用主要控制分布特征而不是证据的神经网络。我们建议在功能集和类之间应用相关性，以及功能之间的相关性来计算相关属性评估度量和协方差度量，以计算新闻项目属性的差异。观察到指标上具有较高值的独特，负，正，正和基本数字可以在曲线（AUC）和F1分数下提供高面积。
Media news are making a large part of public opinion and, therefore, must not be fake. News on web sites, blogs, and social media must be analyzed before being published. In this paper, we present linguistic characteristics of media news items to differentiate between fake news and real news using machine learning algorithms. Neural fake news generation, headlines created by machines, semantic incongruities in text and image captions generated by machine are other types of fake news problems. These problems use neural networks which mainly control distributional features rather than evidence. We propose applying correlation between features set and class, and correlation among the features to compute correlation attribute evaluation metric and covariance metric to compute variance of attributes over the news items. Features unique, negative, positive, and cardinal numbers with high values on the metrics are observed to provide a high area under the curve (AUC) and F1-score.
