# A Deep Learning Approach for Automatic Hate Speech Detection in the Saudi Twittersphere
## 7 Conclusions and Future Direction  
Hate speech in the Arabic Twittersphere has become a notable problem, resulting in a pressing need for effective automatic solution for hate speech detection. In this work, we constructed a public dataset of 9316 tweets labeled as hateful, abusive, and normal. We evaluated and compared four different models: CNN, GRU, CNN + GRU, and BERT. The obtained results from our experiments were promising, showing the effectiveness of the proposed models in the detection task. The results showed that CNN successfully outperformed other models, with an F1-score of 0.79 and AUROC of 0.89. Our results also showed that BERT failed to improve over the baselines and the other evaluated models. This could be attributed to the fact that BERT was trained on different dataset genre (Wikipedia). We believe that there are several ways to extend and improve this study in the future. The dataset can be extended to capture more writing styles, patterns, and topics. The dataset can also be annotated with multi-labels to enhance the results of the detection task beyond the binary classification. For example, annotating the target of the hate speech and the aggressive level (week/strong) could introduce a new direction for future works and applications. We also think that the conducted experiments could be enhanced in different ways. First, we aim to extend our out-domain experiments and evaluate the proposed models on other abusive/offensive language datasets such as those found in [24; 25]. This could help us to understand how to distinguish between hate speech and abusive/offensive language. Moreover, different embedding methods could be investigated besides Word2 Vec, such as ELMo [47], BERT, FastText [48], and the Universal Sentence Encoder (USE) [49]. Other features can be also incorporated with word embeddings such as the user's gender, age, and location. Moreover, to alleviate the lack of the context problem, we found that when we analyzed the errors made by our models we aimed to incorporate the tweets' context (e.g., the original tweet of the replies, quoted tweets, text from the external links) into the feature space and investigate if this could boost the performance of the classifiers. Conceptualization, R.A. and H.A.-K.; methodology, R.A. and H.A.-K.; validation, R.A.; formal analysis, R.A. and H.A.-K.; investigation, R.A.; resources, R.A.; data curation, R.A.; writing--original draft preparation, R.A.; writing--review and editing, H.A.-K. All authors have read and agreed to the published version of the manuscript. * [1] [PERSON]; [PERSON] Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus. In _Proceedings of the Computer Science & Information Technology (CS & IT), Dubai, UAE, 23-24 February 2019_; AIRCC Publishing Corporation: Tamil Nadu, India, 2019; pp. 83-100. * [2] [PERSON]; [PERSON] A Survey on Hate Speech Detection using Natural Language Processing. In _Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media, Valencia, Spain, 3 April 2017_; Association for Computational Linguistics: Valencia, Spain, 2017; pp. 1-10. * [3] [PERSON]; [PERSON] Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter. In _Proceedings of the NAACL Student Research Workshop, San Diego, CA, USA, 12-17 June 2016_; Association for Computational Linguistics: San Diego, CA, USA, 2016; pp. 88-93. * [4] [PERSON]; [PERSON]; [PERSON]; [PERSON] Automated Hate Speech Detection and the Problem of Offensive Language. _arXiv_**2017**, arXiv:1703.04009. * [5] [PERSON]; [PERSON] [PERSON] Right-wing German hate speech on Twitter: Analysis and automatic detection. _arXiv_**2018**, arXiv:1910.07518, Manuscript Submitted. * [6] [PERSON]; [PERSON] A Survey on Automatic Detection of Hate Speech in Text. _ACM Comput. Surv._**2018**, 51, 1-30. [CrossRef]
* [7] [PERSON]; [PERSON] One-step and Two-step Classification for Abusive Language Detection on Twitter.