\section{Introduction}

The spread of disinformation has taken a toll on public trust in news media \cite{lee_disinformation_2024}.
The effects of this are reflected in social and political unrest, such as the 2016, 2020, and 2024 United States presidential elections \citep[see][respectively]{allcott_social_2017, benkler_mail-voter_2020, cansu_2025} and the COVID-19 pandemic \cite{rocha_impact_2021}.
There is a widespread perception that journalists have not only failed to shield the public from disinformation, but have also contributed to its spread by aligning themselves with bad actors \citep{harrington_2024}.
On top of that, there is a belief that news media prioritizes profit over veracity, treating it as some sort of advertisement \citep{Amazeen_2019}.

Although disinformation takes many forms, we focus on deception based on the definitions of news media watchdog organizations, such as Media Bias/Fact Check.\footnote{\url{https://mediabiasfactcheck.com/methodology/}}
These are often determined based on political bias and on the amount of false information, be it of the articles themselves or of the outlets that publish them.
We focus on political news, as it has become a loci of public concerns over the role that news media plays in global politics and the influence disinformation has in it \citep{benkler_mail-voter_2020,harrington_2024}.

Political persuasion and disinformation are closely related \citep{gil_2025}.
We assume that credible news aims to inform, while deceptive news attempts to persuade readers in favour of a certain viewpoint.
We hypothesize that this will be reflected in the argumentation within the articles themselves.
Our research questions are as follows:
\begin{description}
    \item[RQ1:] Can argumentation features be used to detect deceptive news?
    \item[RQ2:] What insights can we acquire by comparing argumentation types in credible and deceptive news?
\end{description}

We implement a two-step approach to test this.
We start by training a BERT \cite{devlin-etal-2019-bert} model to identify argumentation types in English news articles.
We extract argumentation features from this model and feed them to a Bi-LSTM \citep{hochreiter_long_1997} to identify deceptive news. 
We go into more detail of our architecture and related design choices in Section \ref{sec:models}.

We report the results of our experiments in Section \ref{sec:results}.
Our approach outperforms other models from the literature, having less variance compared to the other non-deterministic methods used in our experiments.
We also show that feature-based models can outperform simple transformer baselines.

We do an analysis of the argumentation types between credible and deceptive news in Section \ref{sec:analysis}.
We show that deceptive news tends to present more anecdotes and testimonies, while credible news tends to have more assumptions supported through evidence.

