  Recent advancements in natural language generation has raised serious
concerns. High-performance language models are widely used for language
generation tasks because they are able to produce fluent and meaningful
sentences. These models are already being used to create fake news. They can
also be exploited to generate biased news, which can then be used to attack
news aggregators to change their reader's behavior and influence their bias. In
this paper, we use a threat model to demonstrate that the publicly available
language models can reliably generate biased news content based on an input
original news. We also show that a large number of high-quality biased news
articles can be generated using controllable text generation. A subjective
evaluation with 80 participants demonstrated that the generated biased news is
generally fluent, and a bias evaluation with 24 participants demonstrated that
the bias (left or right) is usually evident in the generated articles and can
be easily identified.
