# Enhancing Detection of Arabic Social Spam Using Data Augmentation and Machine Learning
## 6 Conclusions  
In conclusion, we set out to investigate the problem of online Arabic spam detection on Twitter, motivated by the scarcity of previous studies that address the problem of class imbalance in social media platforms, as well as the scarcity of data augmentation techniques for Arabic text. We first tried to improve the performance of classifiers as much as possible by adding more features that help build better models for spam detection, paying particular attention to the recall of minority classes. We also showed that utilizing pre-trained neural embedding vectors on large corpora, specifically the Word2 Vec (Ara2 vec) model pre-trained on Arabic tweets, was fit for the embedding layer, introducing word context that was not present in the limited datasets. The first improvement presented in experiment Section 4.2, which employed content, interaction, and user features, increased Macro F1 of the best classifier (LR) from 58% to 65%. The added features also improved the recall of the spam class from 55% to 65%, both achieved by SVC, indicating that the selected features were able to improve the performance of our models. The second improvement presented in experiment Section 4.3 was the proposed augmentation method, which achieved an improvement of 24% (from 65% to 89%) for the Macro F1 score, in addition to an increase of 28% (from 65% to 93%) in the spam class recall compared to the non-augmented with added features baselines. LinearSVC was the best performing classifier, which surpasses the other tested classifiers with 92.1% of correctly classified instances, a Macro F1 score value of 89%, and an AUC score value of 96%. We found that we can use these pre-trained embeddings in a vector similarity-based word replacement technique to augment the dataset within the text classification frame  
Figure 7: Plot of the three experiments ROC curve analysis. work, reducing class imbalance proportions, and encouraging the model to capture the context of spam tweets rather than depending on the manual labeling of spam tweets. We believe that various approaches are worth studying for future work on data augmentation for Arabic spam detection tweets. We believe that replacement strategies could be further automated by training a model that predicts which words should be replaced and learning word-specific similarity thresholds rather than a global one. Although the generative method we provided in this research achieved a good improvement over the data augmentation method, we believe that using contextual data augmentation, such as BERT, for data augmentation of Arabic text can be very promising. In addition, it would be interesting to show how the data augmentation strategies we described perform when using deep learning techniques or NLP applications other than the spam detection domain. Conceptualization, A.M.A., A.E. and C.A. ; data curation, A.M.A. ; formal analysis, A.M.A. ; investigation, A.M.A., A.E. and C.A. ; methodology, A.M.A., A.E. and C.A. ; resources, A.M.A. ; software, A.M.A. ; validation, A.M.A., A.E. and C.A. ; writing--original draft preparation, A.M.A. ; writing--review and editing, A.E. and C.A. ; supervision, A.E. and C.A. ; visualization, A.M.A., A.E., and C.A. ; work administration, A.E. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Available upon request. The authors declare no conflict of interest.