  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.
