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Learning to Flip the Bias of News Headlines

This paper introduces the task of {``}flipping{''} the bias of news articles: Given an article with a political bias (left or right), generate an article with the same topic but opposite bias. To study this task, we create a corpus with bias-labeled articles from \textit{all-sides.com}. As a first step, we analyze the corpus and discuss intrinsic characteristics of bias. They point to the main challenges of bias flipping, which in turn lead to a specific setting in the generation process. The paper in hand narrows down the general bias flipping task to focus on bias flipping for news article \textit{headlines}. A manual annotation of headlines from each side reveals that they are self-informative in general and often convey bias. We apply an autoencoder incorporating information from an article{'}s content to learn how to automatically flip the bias. From 200 generated headlines, 73 are classified as understandable by annotators, and 83 maintain the topic while having opposite bias. Insights from our analysis shed light on how to solve the main challenges of bias flipping.

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