## 4 Proposed Hate Speech Detection Architecture
### Proposed Odia-English Text Preprocessing
#### 4.1.1 Removing (Cleaning) Irrelevant Character, Punctuations Symbol, and Emoji's
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Reference/Year**} & \multirow{2}{*}{**Name/Language**} & \multirow{2}{*}{**Number of Instances**} & \multirow{2}{*}{**Labels in Dataset**} & \multirow{2}{*}{**Source**} & \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Performance**} \\ \cline{5-8}  & & & & & & & **A** & **F1** \\ \hline \multirow{3}{*}{[24], 2017} & \multirow{3}{*}{English} & \multirow{3}{*}{25000} & Hate, & \multirow{3}{*}{Twitter} & Unigram, bigram, and & LR, NB, DT, & \multirow{3}{*}{–} & \multirow{3}{*}{0.90} \\  & & & offensive and & & & TF-IDF & & & \\ \hline \multirow{3}{*}{[29], 2017} & \multirow{3}{*}{Indonesian} & \multirow{3}{*}{1100} & HS and & \multirow{3}{*}{Twitter} & BOW, **word n-gram** and & NB, SVM, & \multirow{3}{*}{–} & \multirow{3}{*}{0.93} \\  & & non-HS & & & character n-gram & & & & \\ \hline \multirow{3}{*}{[33], 2018} & \multirow{3}{*}{English} & \multirow{3}{*}{45,645—} & \multirow{3}{*}{Not labelled} & \multirow{3}{*}{Twitter} & Unigram, & \multirow{3}{*}{NB} & \multirow{3}{*}{0.70} & \multirow{3}{*}{–} \\  & & & testing & & & & & \\ \hline \multirow{3}{*}{[22], 2018} & \multirow{3}{*}{Ahmaric in} & \multirow{3}{*}{6,120} & Hate and not & \multirow{3}{*}{Facebook} & word2 vec and TF-IDF & **NB**, RF & \multirow{3}{*}{0.798} & \multirow{3}{*}{0.853} \\  & & & hate & & & & & \\ \hline \multirow{3}{*}{[44], 2018} & \multirow{3}{*}{English} & \multirow{3}{*}{5143} & Hate and not & \multirow{3}{*}{Hate} and not & Comments & n-grams, semantic and & \multirow{3}{*}{} & \multirow{3}{*}{} \\  & & & hate and not & posted in & syntactic, **TF-IDF**, & & & \\ \cline{1-1} \cline{5-8}  & & & hate and & YouTube and & word2 vec embeddings, & & & \\ \cline{1-1} \cline{5-8}  & & & Facebook & & & & & \\ \hline \multirow{3}{*}{[35], 2018} & Crowflower & \multirow{3}{*}{14509} & Hateful, & \multirow{3}{*}{Twitter} & N-gram and **TF-IDF** & **LR**, NB, SVM & \multirow{3}{*}{0.956} & \multirow{3}{*}{0.96} \\  & & & clean & & & & \\ \hline \multirow{3}{*}{[18], 2018} & \multirow{3}{*}{\
\begin{tabular}{c} Hindi-English \\ code mixed \\ data \\ \end{tabular} } & \multirow{3}{*}{4575} & Hate and & \multirow{3}{*}{Twitter} & Character n-gram, word & \multirow{3}{*}{**SVM**, RF} & \multirow{3}{*}{0.717} & \multirow{3}{*}{–} \\  & & & normal & lexicon, **all features** & & \\ \hline \multirow{3}{*}{[20], 2020} & \multirow{3}{*}{
\begin{tabular}{c} Hindi-English \\ code mixed \\ data \\ \end{tabular} } & \multirow{3}{*}{10000} & Hate and & Twitter, & \multirow{3}{*}{**FastText**, word2 vec & SVM- linear, & \multirow{3}{*}{0.8581} & \multirow{3}{*}{0.8580} \\  & & non-hate & HASOC & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Contemporary Hate speech Detection Models. better performance than the NB and RF models. The ternary models performed better in handling misclassification between the hate and non-hate posts and comments than the binary models. Furthermore, the ternary SVM model with word2 vec resulted in a 53% F1-score, which showed a better performance than the models with NB and RF. Finally, the models based on SVM using word2 vec yielded slightly better performances than the NB and RF models for both the datasets used in this research. Conceptualization, S.K.M. and S.P. ; methodology, S.K.M. ; software, S.K.M. ; validation, D.K.B., S.P. and Y.-C.H. ; formal analysis, S.K.M. and Y.-C.H. ; investigation, S.P. ; data curation, D.K.B. ; writing--original draft preparation, T.K.D. ; writing--review and editing, K.S. and Y.-C.H. ; visualization, T.K.D. ; supervision, K.S. ; project administration, Y.-C.H. ; funding acquisition, Y.-C.H. All authors have read and agreed to the published version of the manuscript. This research was not funded. Not applicable. The authors declare no conflict of interest. ## References

* [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] (2021)Automated Classification of Evidence of Respect in the Communication through Twitter. Appl. Sci.2021 (11), pp. 1294.