\section{Hyperparameters of the Models}
\label{app:hyperparameters}

In this appendix we present the hyperparameters and other implementation details from our models.


\subsection{Argumentation Type Classifier}

The argumentation type classifier we used was implemented using the HuggingFace\footnote{\url{https://huggingface.co/}} package for python\footnote{\url{https://www.python.org/}} using a PyTorch\footnote{\url{https://pytorch.org/}} backend.

We used the model \verb|bert-base-uncased| from the Transformers package.
For this, we used the class \verb|AutoModelForSequenceClassification|.

The hyperparameters used were the default ones except for the following ones:

\begin{itemize}
    \item Evaluation strategy: steps
    \item Evaluation steps: 100
    \item Evaluation delay: 1
    \item Number of training epochs: 3
    \item Load best model at the end: True
    \item Per device training batch size: 8
\end{itemize}


\subsection{Deceptive News Classifier}

The deceptive news classifier was implemented in PyTorch using the Adam \cite{kingma_adam_2019} optimizer.
We used a single Bi-LSTM layer followed by a linear layer.
The last hidden states from each direction were concatenated and then fed to the linear layer for classification.

The hyperparameters we used were the following:

\begin{itemize}
    \item Learning rate: 1e-4
    \item LSTM hidden dimension: 64
    \item Batch size: 32
    \item Dropout: 0.5
    \item Max number of epochs: 2000
    \item Early stopping at n steps: 15
\end{itemize}

