\section*{Limitations}
\label{sec:limitation}

A possible limitation of our work could be the scope of the data.
To the best of our knowledge, the Webis-16 dataset is one of the most thoroughly annotated news media datasets for argumentation types.
However, the editorials it contains come from only three publishers.
Despite this, we achieve good results in our downstream application.
It is also important to note that \citet{lindahl-2024-disagreement} argues that it can be complicated to annotate argumentation in text due to ambiguity or multiple plausible interpretations.

Moreover, the annotations of this dataset do not take veracity into account.
This makes it so that we can properly model argumentation on its own, without introducing biases in the deceptive news classification task.
It is not possible to do joint training for the whole pipeline for that reason.

In a similar vein, the data we use for the deceptive news detection task comes predominantly from English outlets in the United States, Canada, and the United Kingdom.
Furthermore, previous studies show that features of deceptive news can vary depending on news topic \citet{francis_variation_2024}.
Therefore our results might not generalize well to other languages, cultural contexts, or topics.

Regardless of these limitations, we consider our results to be useful in showcasing how other areas of NLP can give us a deeper insight into how deceptive news works.
We encourage people using our methodology in different linguistic or cultural contexts to verify that is is an appropriate approach before doing any sort of implementation.
