## 6 Conclusions  
This paper presented a study on the impact of data pre-processing on hate speech detection on social media platforms, in particular, Twitter. Data cleansing included 10 steps, such as the removal of URLs and hashtags. An efficient method was developed to accurately detect hate speech in text written in a mix of multiple languages including code-mixed, i.e., English and Hindi-English. The proposed method incorporates character-level embedding representations of text and deep neural networks (DNNs). Particularly, two model architectures were investigated, namely CNN and CNN-LSTM. A real-life dataset of published tweets was used to evaluate the proposed method in terms of accuracy, runtime, and scalability. The pre-processed dataset was split into training and test sets, where imbalanced and over-sampled versions of the training set were used to train the model. The experimental results suggest that data pre-processing significantly reduces runtime, without compromising the accuracy of prediction. Moreover, both the CNN and CNN-LSTM models trained with the over-sampled version of the training dataset increased the accuracy of prediction, outperforming a closely related method in the literature, i.e., [PERSON] et al.'s model [7].  
Figure 10: Scalability comparison of different models.  
Figure 9: Runtime comparison of different models.  
Among our best-performing models, CNN-LSTM performed slightly better than CNN with an accuracy of 88.97%.  
In the future, in addition to detecting hate speech in a mix of English and Hindi-English code-mixed tweets, the proposed method can be extended to include various other languages--pure and code-mixed. Moreover, a future research direction would be to consider the psychological effect of emoticons on social media users and study the significance of emoticons on hate speech. This can be achieved by: 1. Studying the impact of various emoticons on human perception, and 2. Integrating emoticons into building data models, which possess the ability to extract the emoticons' contextual meaning within the provided text.  
Conceptualization, M.S.; Data curation, M.S.; Formal analysis, K.A.-H., M.S. and I.K.; Funding acquisition, K.A.-H.; Investigation, M.S.; Methodology, K.A.-H. and M.S.; Project administration, K.A.-H.; Resources, K.A.-H.; Software, M.S.; Supervision, K.A.-H.; Validation, K.A.-H. and I.K.; Visualization, M.S. and I.K.; Writing--original draft, M.S.; Writing--review and editing, K.A.-H. and I.K. All authors have read and agreed to the published version of the manuscript.  
This research is supported in part by the DSO-RIT Dubai Research Fund (2022-23-1003) from Rochester Institute of Technology--Dubai (RIT-Dubai).  
Not applicable.  
All the datasets used in this study are publicly available and are referenced in Section 3.  
The authors would like to thank Rochester Institute of Technology--Dubai (RIT-Dubai) for supporting this research effort.  
The authors have no competing interest to declare that are relevant to the content of this article.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CNN & Convolutional Neural Network \\ LSTM & Long Short-Term Memory \\ BiLSTM & Bi-Directional LSTM \\ RF & Random Forest \\ SVM & Support Vector Machine \\ LSVM & Linear SVM \\ \end{tabular}