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Recent advancements in natural language generation has raised seriousconcerns. High-performance language models are widely used for languagegeneration tasks because they are able to produce fluent and meaningfulsentences. These models are already being used to create fake news. They canalso be exploited to generate biased news, which can then be used to attacknews aggregators to change their reader's behavior and influence their bias. Inthis paper, we use a threat model to demonstrate that the publicly availablelanguage models can reliably generate biased news content based on an inputoriginal news. We also show that a large number of high-quality biased newsarticles can be generated using controllable text generation. A subjectiveevaluation with 80 participants demonstrated that the generated biased news isgenerally fluent, and a bias evaluation with 24 participants demonstrated thatthe bias (left or right) is usually evident in the generated articles and canbe easily identified.

Viable Threat on News Reading: Generating Biased News Using Natural Language Models