\section{Number of Labels of the Argumentation Dataset}
\label{app:labels}

During our preliminary exploration of the argumentation type classifier that the least represented class was getting misclassified in all of our experiments.
Thus we decided to explore the possibility of collapsing some of the least represented labels into a single one.

We took into account the macro and weighted scores of the model, as well as the F1 score of the least represented class.
An important criterion when selecting the number of labels was to keep as many labels as possible.
This is particularly important as we want both our deceptive news classifier to learn the most out of the argumentative structure of the articles.
Moreover, we want to be able to look at the argumentation types in the articles to get further insights.

As we can see from Figure \ref{fig:arg_boxplots}, the less labels we keep, the better the performance of the model.
This was to be expected given that the more labels there are available, the less likely a model is to get a correct result if it is choosing randomly.

When looking a the validation scores (see Figure \ref{fig:arg_boxplot}) for the deceptive news classification task, we realize that the models that kept just four labels model does slightly better than the others in average.
However, it is important to note that the boxplots for all groups overlap.

We decided to keep four labels as opposed to two or three as we believe that it would help with when qualitative analysis from Section \ref{sec:analysis}, while keeping more labels would mean that there is a risk that neither the argumentation nor the deceptive news classifiers would work as well as they would otherwise.

% Separated the big figure from the smaller ones to have more leeway when doing the final layout for the camera-ready paper
\input{paper/inserts/B_arg_boxplot_1}
\input{paper/inserts/B_arg_boxplot_2}