# Post-Authorship Attribution Using Regularized Deep Neural Network
## 4 Experimental Results
### Results
#### 4.3.1 Quantitative Results  
The quantitative results based on accuracy and F1-score performance measures are shown in Table 4. Most of the methods did not compute the measures across the four experimental benchmarked datasets. The RDNN method recorded good accuracies and F1-scores for three of the experimental datasets when compared to other SOTA methods. The proposed method concomitantly gave the highest accuracy and F1-score values of 93.20% and 92.20%, respectively, on the CCAT50 dataset. It recorded the highest F1-score of 76.69% and the second-best accuracy score of 63.40% on the Blogs50 dataset. In addition, it scored the highest accuracy of 86.48% with the second-best F1-score of 84.80% on the Twitter50 dataset. However, it came second in accuracy (96.50%) and third in F1-score (95.75%) on the IMDB62 dataset when compared to the 22 SOTA methods investigated. Its results are highly comparable with the top-performing methods with the highest accuracy score of 97.72 [1], and F1-score of 97.90 [87] on the IMDB62 dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layers** & **Parameters** & **CCAT50** & **IMDB62** & **Blogs50** & **Twitter50** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} CNN \\ Character-level \\ \end{tabular} } & \(d\) & 30 & 70 & 70 & 70 \\  & \(f\) & ReLU & ReLU & ReLU & RELU \\  & \(\delta_{o}\) & 0.5 & 0.5 & 0.5 & 0.5 \\  & \(p_{s}\) & 2 & 2 & 2 & 2 \\ \hline BLSTM & \(m\) & 128 & 128 & 256 & 512 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} FCL \\ \end{tabular} } & \(l\) & 2 & 2 & 2 & 2 \\  & \(\delta_{o}\) & 0.7 & 0.5 & 0.5 & 0.5 \\  & \(m\) & 512 & 512 & 128 & 128 \\  & \(\sigma\) & SoftMax & SoftMax & SoftMax & SoftMax \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The setting of hyperparameters in RDNN method on different datasets. The effectiveness of the RDNN method is further presented graphically based on the accuracy and loss curves for visual validation purposes. Figures 2-5 show the accuracy and loss curves for the CCAT50, IMDB62, Blogs50, and Twitter50 datasets, respectively. It can be observed from the figures that the proposed method generally has better performance in accuracy with faster convergence speed in the training and testing processes on all the datasets. However, complex interactions between different stylistic features with nonlinearities can be observed in the Twitter50 datasets. Although performance improvement is achieved with the RDNN method, the effect of certain features such as hashtags is highly indicative. This blurs the boundaries of the proposed method in understanding the writing style of numerous authors. The Twitter50 dataset reveals a bias in the following ways. We did not preprocess the input post but converted it to lower case to erode the character sensitivity effect of a possible discrepancy between upper- and lower-case letters. The RDNN intrinsically leverages the consequential effects of uniform resource locators, hashtags, and mentions of names in the posts to achieve high accuracy and F1 score. The celebrities and power-users used different vocabularies in posts, as reflected in Figures 2 and 5. This was, for instance, by frequently tweeting similar messages to enhance their brands in comparison to regular users or bloggers who were interested in news, as shown in Figure 4. However, this behavior makes it hard for the proposed method to consistently learn the writing style of an author. It would be prudent for future work to apply byte-pair encoding to investigate the stylistic differences between regular authors and celebrities with their impact on PAA methods [102].