In this work, we focus on the problem of distinguishing a human written news article from a news article that is created by manipulating entities in a human written news article (e.g., replacing entities with factually incorrect entities). Such manipulated articles can mislead the reader by posing as a human written news article. We propose a neural network based detector that detects manipulated news articles by reasoning about the facts mentioned in the article. Our proposed detector exploits factual knowledge via graph convolutional neural network along with the textual information in the news article. We also create challenging datasets for this task by considering various strategies to generate the new replacement entity (e.g., entity generation from GPT-2). In all the settings, our proposed model either matches or outperforms the state-of-the-art detector in terms of accuracy. Our code and data are available at https://github.com/UBC-NLP/manipulated_entity_detection.

该研究提出了一种基于神经网络的检测器，利用图卷积神经网络和新闻文章中的文本信息来探究文章提到的事实，以区分人工编写的新闻文章和通过对实体进行事实不正确的替换而生成的文章。研究者使用了多种策略生成新实体替换，包括使用GPT-2生成。该模型在所有设置中的准确性均达到或超过了最先进的检测器。

利用事实知识自动检测实体篡改文本