Multilingual Coarse Political Stance Classification of Media. The
  Editorial Line of a ChatGPT and Bard Newspaper
Cristina España-Bonet
Neutrality is difficult to achieve and, in politics, subjective. Traditional
media typically adopt an editorial line that can be used by their potential
readers as an indicator of the media bias. Several platforms currently rate
news outlets according to their political bias. The editorial line and the
ratings help readers in gathering a balanced view of news. But in the advent of
instruction-following language models, tasks such as writing a newspaper
article can be delegated to computers. Without imposing a biased persona, where
would an AI-based news outlet lie within the bias ratings? In this work, we use
the ratings of authentic news outlets to create a multilingual corpus of news
with coarse stance annotations (Left and Right) along with automatically
extracted topic annotations. We show that classifiers trained on this data are
able to identify the editorial line of most unseen newspapers in English,
German, Spanish and Catalan. We then apply the classifiers to 101
newspaper-like articles written by ChatGPT and Bard in the 4 languages at
different time periods. We observe that, similarly to traditional newspapers,
ChatGPT editorial line evolves with time and, being a data-driven system, the
stance of the generated articles differs among languages.