\section{Conclusions}
\label{sec:conclusion}


Factuality in news media is closely related to similar phenomena, such as partisan bias, propaganda, and satire \cite{ruffo_studying_2023}.
The rapid spread of deceptive news and misinformation has been linked to instability in the global political climate, as well as erosion of trust in news media \cite{lee_disinformation_2024}.
\cite{Gelfert_2018} and \cite{harrington_2024} argue that it is important to study these complex phenomena in order to mitigate the risks and consequences they engender.

In this paper we hypothesized that argumentation in credible and deceptive political news articles would differ as a reflection of their role as informers or vectors for ideology.
We proposed an approach exploiting argumentation types of sentences within an article to detect deceptive news.
On average, our approach outperformed three models from the existing literature, namely BERT, an SVM with LIWC features, and FakeFlow.
It also shows a lower variance than the non-deterministic baselines.

Some interesting patterns appear when analyzing the distributions of argumentation types.
We found that deceptive articles tend to use more testimonies and, for one of the datasets, more anecdotes.
Although credible news tend to have more assumptions, they appear to support them with evidence or by establishing shared knowledge.
This matches previous findings from the literature that point out that deceptive news uses logical fallacies, such as overusing anecdotes or by appealing to authority \citep{yu-etal-2021-interpretable}.

It is important to note that the work we present in this paper is not any sort of ``truth detector''.
Our model was trained and tested to be used in news articles and should only be used for that kind of media.
The datasets have binary truth annotations and were curated with that purpose in mind.
This means that things living in the in-between of truth and falsehood might potentially be misrepresented.
Moreover, there are different kinds of mis- and disinformation (such as propaganda or hyper-partisan news) that are not explicitly studied in the present paper to better isolate features pertaining deceptive news.

The results of this study show that stylistic features, such as argumentation type, can improve classification performance and enrich our understanding of complex phenomena such as deceptive news and misinformation.
Not only that, but they can also help develop systems that are both more interpretable and perform as well as other classification systems, if not better.

It is also important to note that we focus on the style of the text rather than on its content.
One of our assumptions is that outlets publishing deceptive content online do so knowingly.
This ignores the possibility that people who write deceptive news articles legitimately believe what they are writing.
It also ignores propaganda in news media that is often regarded as trustworthy, be it backed by the State and/or by for-profit organizations.
