# Post-Authorship Attribution Using Regularized Deep Neural Network
## 4 Experimental Results
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However, they achieved a comparatively worse accuracy for short messages such as the Blogs50 and Twitter50 datasets. It can be suspected that SCAP stores discrete n-grams for each author, which makes it simple to distinguish a topic preference for each author. However, it is unsatisfactory to capture the writing style of different authors or identify several posts written by an author. If such a correlation exists, the low accuracy reached by the CNN [77], SRNN [78], and DeepStyle [85] proves that the models carry less topical information. Moreover, the stylistic representation loses cohesiveness when the number of authors is very large. The RDNN method is still superior in capturing better stylistic information such as words, topics, punctuation, or terms. In terms of the average F1-score, the RDNN method outperforms the comparative SOTA methods on CCAT50, Blogs50, and Twitter50 datasets by a margin of 21.9%, 19.9%, and 2.0%, respectively. However, the BertAA model is highly competitive on the IMDB62 dataset by roughly 2.15%. The word-based methods such as SVM, BertAA, CNN-char, and CNN-word perform well on CCAT50 and IMDB62 datasets, in which topical information is unrejudiced. However, our method achieves a comparative performance boost for Blogs50 and Twitter50 datasets. Even the shallow neural network of FastText embedding [60] does not beat the SVM histogram [46] with similar feature sets. Nevertheless, our PAA method shows superior encoding ability for the task of authorship attribution on both CCAT50 and IMDB62 datasets. The fine-tuned BERT, BertAA, and continuous n-gram representation model with pre-embedding vectors are sensitive to topical writing divergence between authors, which boosts performance on the IMDB62 dataset. However, they are less effective on the Twitter50 dataset than the CNN-char, where hashtags or emoticons are the most distinctive features. Moreover, our method outperforms the SVM model with frequent 3-g, LDAH-S, Imposters, CNN variants, and other deep neural-network-learning methods on three of the four datasets. This is in terms of accuracy and F1-score performance measures, except for the Twitter50 dataset, where CNN-char outperforms our PAA method in F1-score by 2% for 50 authors. The experimental results have demonstrated the aptitude of the RDNN method to handle the class-variation problem inherent in real-world text-communication applications [21]. The visualization plots of Figures 10 and 11 show the prediction for just one example of a testing dataset using the RDNN method. The SHAP values from the \"based\" value represent the features that are predicted from the RDNN method to reflect the writing style of an author. The analysis of how SHAP values are attributed to the writing style of an author is provided in Figure 12a,b. In this case, all features are continuous and vertically sorted by the average impact on the predictions. It can be observed that \"_china_\" and \"_film_\" have the maximum impact on the prediction with high mean values on CCAT50 and IMDB62 datasets. Similarly, high mean values recorded by \"_wang_\", \"_bji_\", \"_cast_\" and \"_movi_\" on CCAT50 and IMDB62 datasets are significant and related to the attributions of a writing style of an author in concordance with Figures 10 and 11.