
\section{How Do People Argue in Deceptive News?}
\label{sec:analysis}


Given an argumentation type label and an article, we look at the fraction of sentences within the article with that label.
We then compare how these values are distributed in each deceptive news dataset (see Figure \ref{fig:distributions}).
To ensure balanced sample sizes for the analysis, we under-sample the most represented class for each dataset.

We use a two-tailed Kolmogorov–Smirnov test \cite{hodges_significance_1958} to determine whether the distributions for credible and deceptive news are different\footnote{Given that we are dealing with distributions of ratios, we can safely assume that they are not normally distributed.} and, if so, how much they differ.
It is important to note that all four distributions are related to each other as the ratios for a given article must sum up to one.\footnote{This is because each of the sentences in an article must have one and only one of the four argumentation type labels.}
Thus, we must apply a Bonferroni correction for $n=4$.
That means that we need a p-value of 0.0125 instead of 0.05 to be able to reject the null hypothesis that the distributions for the credible and deceptive news articles are the same.

\input{paper/inserts/07_KS}

The Kolmogorov–Smirnov statistic, shown in Table \ref{tab:KS}, tells us the largest difference between the two cumulative distribution functions.
Excluding \emph{anecdote} in the FakeNews-2018 dataset and \emph{assumption} in PolitiFact, the results are statistically significant and show a large difference. 

We will go over the differences between the distributions of the four labels, focusing on the FakeNews-2018 dataset as we consider that these distributions can give us potentially interesting insights.

When looking at the distribution of the \emph{anecdote} argumentation type, we notice that anecdotes appear more often in articles labelled \emph{fake} in the PolitiFact dataset.
Usage of anecdotes may be a strategy used by deceptive news outlets to strengthen arguments in lieu of factual evidence.
Previous literature has also noted that more persuasive articles use logical fallacies, such as arguments from anecdote, which leverage readers' emotions \citep{yu-etal-2021-interpretable}. Meanwhile the Komogorov-Smirnov statistic shows that the difference on the FakeNews-2018 dataset is small and not statistically significant.

In general, the label \emph{assumption} is the most evenly distributed across the articles, regardless of the dataset or whether they are deceptive or not.
As we can see in Figure \ref{fig:distributions}, assumptions are less represented in deceptive news in the FakeNews-2018 dataset.

The label \emph{assumption} appears more often in articles, regardless of whether they are deceptive or not.
In contrast, the other labels tend to represent a small proportion of the sentences of an article.
This does not mean that there are no differences between credible and deceptive news, as assumptions are less represented in deceptive news in the FakeNews-2018 dataset (as shown in Figure \ref{fig:distributions}).
\citet{Gelfert_2018} notes that the modern wave of disinformation stems partly from conspiracy theories.
Conspiracy theorists avoid making explicit assumptions to avoid accountability for their claims, using the excuse of ``just asking questions'' \cite{Egelhofer_2019}.
As mentioned previously, the difference between the distribution for credible and deceptive news is not statistically significant for the PolitiFact dataset, likely due to the small size of the dataset.

The \emph{testimony} label is represented more in deceptive news for both datasets. Figure \ref{fig:distributions} shows this for the FakeNews-2018 dataset.
This may be related to the use of news as a medium for political advertising \cite{Nelson_2021}.
Studies have shown that testimonials positively impact consumer bias and that consumers identify more strongly with testimonials from individuals they consider peers \citep{Shimp, Appiah}.
It has also been observed that partisan loyalty has an effect on believability, as readers are more likely to report information from sources that share their political affiliation as factual \citep{Morris_2020,Landreville_2019}.
On the other hand, this could also be due to deceptive news using fallacious strategies such as appealing to authority \citep{yu-etal-2021-interpretable}.

The \emph{other} label is represented the least in both datasets and both types of news, but appears more in credible news articles than in deceptive ones.
It is important to note that the \emph{other} label contains the \emph{statistics} and \emph{ground-truth} labels from the original Webis-16 dataset (as noted in Section \ref{sec:dataset_arg}).
This suggests that credible news substantiates claims more often than deceptive.

As mentioned previously in this section, Figure \ref{fig:distributions} shows the distributions for the labels \emph{assumption} and \emph{testimony} in the FakeNews-2018 dataset.
The histograms comparing the distributions for all the argumentation type labels can be found in Appendix \ref{app:hist}.