## 5 Comparative Analysis Results and Discussion
### Hyperparameter Finetuning Results
#### 5.3.1 Convolution Layer  
There are several layers of filtering and pooling in the initial part of a CNN. Feature maps are created by applying these layers to an array of multi-dimensional features [62]. These features represent abstractions of the most significant characteristics contained in the input data. This is followed by a fully connected network that maps the extracted features to their respective targets [63]. In classification problems, this target represents \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Architecture** & **Accuracy** & **F1 Score** & **Precision** & **Recall** \\ \hline BERT + CNN & 12 Encoder & & & & & \\ (ClaimsBERT) & 2 Convolution & **0.973** & **0.96** & **0.963** & **0.966** \\  & 2 Dense & & & & & \\ \hline BERT + NN & 12 Encoder & & & & & \\ (CyBERT [8]) & 12 Encoder & & & & & \\ (CyBERT [8]) & 3 Dense & & & & & \\ (CyBERT [8]) & 12 Encoder & & & & & \\ (CyBERT [8]) & 12 Encoder & & & & & \\ (CyBERT [8]) & 3 Dense & & & & & \\ (CyBERT [8]) & 12 Encoder & & & & & \\ (CyBERT [8]) & 0.952 & 0.942 & 0.947 & 0.938 \\  & 2 Dense & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparing classification performance of ClaimsBERT with other network-based models. The bold row indicates best model parameters with highest accuracy. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Architecture** & **LR** & **Accuracy** & **F1 Score** \\ \hline \hline BERT + 1 MLP & 1 Dense & \(4\times 10^{-5}\) & 0.936 & 0.925 \\  & 2 Dense & \(6\times 10^{-5}\) & 0.942 & 0.932 \\  & 3 Dense & \(3\times 10^{-6}\) & 0.941 & 0.935 \\  & 1 Dense & \(4\times 10^{-6}\) & 0.943 & 0.935 \\ BERT + 2 MLP & 2 Dense & \(3\times 10^{-6}\) & 0.932 & 0.921 \\  & 3 Dense & \(2\times 10^{-5}\) & 0.945 & 0.931 \\  & 1 Dense & \(4\times 10^{-6}\) & 0.93 & 0.923 \\
**BERT + 3 MLP** & **2 Dense** & \(5\times 10^{-6}\) & **0.952** & **0.942** \\  & 3 Dense & \(3\times 10^{-5}\) & 0.925 & 0.911 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparing BERT + MLP Classifier results. (Bold text indicates the model parameters achieving the highest accuracy. )data that share common characteristics. CNNs flatten out the multi-dimensional output of convolutional components and pass it on to the fully connected network. For the CNN hyperparameters, we investigated the effects of different values, such as the number of convolutions, as well as different filter and kernel sizes for each convolution layer. The filter size in the convolution layer determines the dimension of the output space, whereas the kernel size specifies the length of the convolution window. The output of the convolution layer has a shape of:  
\[(m-k+1,n), \tag{4}\]  
where \(n\) is the number of filters, \(k\) is the kernel size of the filters, and m indicates the number of words in each sentence [62]. The number of filter weights (i.e., parameters) in the convolution layer are:  
\[n*(k*w)+n, \tag{5}\]  
where \(n\) is the number of filters, \(k\) is the kernel size, and \(w\) is the embedding dimension [62]. We also investigated the effects of filter size on the training process and the classifier model accuracy. Different architectures with different filter sizes, including 256, 128, 64 and 32, were tested to find the most effective model. We experimented starting with a BERT base model, expanded it with different numbers of convolution layers, followed by dense layers for each model. In order to determine the configuration maximizing the accuracy of the classifier we utilized a parameter sweep methodology. With this approach we started at a configuration of one convolution layer and one dense layer and swept both parameters upwards until we found the peak accuracy. We validated the peak by observing the configuration that showed a reduction in accuracy. The resulting sweep spanned the range of one to four dense layers and one to three convolutional layers. The results presented in Table 7 show the results from that sweep operation. The input sequence length is 68 for all models.