\input{paper/inserts/E_lengths}
\section{Analyzing the Length of News Articles}
\label{app:length}

While looking through the datasets during our preliminary exploration we noticed that the length of the articles varied greatly between credible and deceptive ones.
The distributions of the lengths of articles can be seen in Figure \ref{fig:fnn_length}.
This length is seen in terms of tokens according to the sentence tokenizer from NLTK.\footnote{\url{https://www.nltk.org/api/nltk.tokenize.sent_tokenize.html}}

We decided to only maintain articles up to a certain length for two reasons.
The first one is that we want to focus on the argumentation types within an article as a way of identifying whether it is deceptive or not.
One way to ensure this is controlling for variables that are not relevant to our hypothesis but that a model might pick up and learn spurious correlations from, such as the length of an article.
The other reason is that the length of an article impacts how its discourse units interlock \citep{van_dijk_dimensions_1989,yarlott-etal-2018-identifying}, meaning that argumentation will differ from shorter to longer texts.

We decided to maintain articles from 100 to 800 for the PolitiFact dataset those from 100 to 500 for the FakeNews-2018 dataset as this is where the summary statistics for both distributions start to converge.