\begin{table*}[tb]
\centering
\begin{tabular}{c|cccc}
\toprule
 & \textbf{F1 Macro} & \textbf{F1 Weighted} & \textbf{Accuracy} & \textbf{Min F1 Score}   \\
 \midrule
\begin{tabular}[c]{@{}c@{}}Majority\\ Baseline\end{tabular} & 0.21 & 0.605 & 0.722 & 0  \\ [3.5mm]
\begin{tabular}[c]{@{}c@{}}Random\\ Baseline\end{tabular}   & 0.249 $\pm$ 0.006         & 0.556 $\pm$ 0.008          & 0.558 $\pm$ 0.01           & 0.038 $\pm$ 0.01  \\[3.5mm]
\textbf{BERT}                                                        & \textbf{0.69 $\pm$ 0.003} & \textbf{0.842 $\pm$ 0.001} & \textbf{0.844 $\pm$ 0.002} & \textbf{0.465} \\
\bottomrule
\end{tabular}
\caption{
Results from our argumentation type classification task.
We report the average accuracy and both the macro and weighted F1 scores across 5 runs, as well as the standard deviation.
We also report the F1 score for the minimum class to ensure the model works reasonably well across all labels.
}
\label{tab:arg_results}
\end{table*}

\begin{table*}[tb]
\centering
\begin{tabular}{c|ccc}
\toprule
 & \textbf{F1 Macro}       & \textbf{F1 Weighted}    & \textbf{Accuracy}       \\
\midrule
BERT          & 0.486 $\pm$ 0.054 & 0.537 $\pm$ 0.047 & 0.540 $\pm$ 0.046 \\[1mm]
\begin{tabular}[c]{@{}c@{}}SVM with\\ LIWC Features\end{tabular} & 0.747 & 0.772 & 0.773 \\[3.5mm]
FakeFlow & 0.780 $\pm$ 0.094 & 0.806 $\pm$ 0.075 & 0.814 $\pm$ 0.056 \\[1mm]
\textbf{\begin{tabular}[c]{@{}c@{}}Argumentation\\ Features (ours)\end{tabular}} & \textbf{0.868 $\pm$ 0.027} & \textbf{0.880 $\pm$ 0.024} & \textbf{0.881 $\pm$ 0.024} \\
\bottomrule
\end{tabular}
\caption{
Results from the deceptive news classification task on the PolitiFact dataset.
We report the average accuracy, both the average F1 macro and weighted scores across 25 runs, and the standard deviation.
Our approach (in bold) outperforms all the baselines we compared to.
Of note is that the standard deviation of our model is also smaller than that of the other probabilistic models we are comparing with.
}
\label{tab:fnn_results}
\end{table*}

\begin{table*}[tb]
\centering
\begin{tabular}{c|ccc}
\toprule
 & \textbf{F1 Macro}       & \textbf{F1 Weighted}    & \textbf{Accuracy}       \\
\midrule
BERT          & 0.496 $\pm$ 0.007 & 0.557 $\pm$ 0.006 & 0.570 $\pm$ 0.014 \\[1mm]
\begin{tabular}[c]{@{}c@{}}SVM with\\ LIWC Features\end{tabular} & 0.856 & 0.870 & 0.873 \\[3.5mm]
FakeFlow      & 0.407 $\pm$ 0.021 & 0.427 $\pm$ 0.030 & 0.415 $\pm$ 0.027 \\[1mm]
%\midrule
\textbf{\begin{tabular}[c]{@{}c@{}}Argumentation\\ Features (ours)\end{tabular}} & \textbf{0.957 $\pm$ 0.009} & \textbf{0.961 $\pm$ 0.008} & \textbf{0.961 $\pm$ 0.008} \\
\bottomrule
\end{tabular}
\caption{
Results from the deceptive news classification task on the FakeNews-2018 dataset.
We report the average accuracy and both the average F1 macro and weighted scores across 25 runs, as well as the standard deviation.
Our approach (bold) outperforms all the baselines we compare it to. The standard deviation of our model is also smaller than that of the other models we compare it with.
}
\label{tab:emi_results}
\end{table*}