## 3 Datasets
### Data Collection  
The Odia-English textual data are the posts and comments gathered from different categories of popular public pages on the Facebook platform, because Facebook's privacy \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors** & **Objective** & **Feature Extraction** & **ML. Method \& Accuracy** \\ \hline \multicolumn{4}{c}{**Binary Class Hate speech**} \\ \hline [PERSON] and [PERSON] [22] & Detection of HS in the Amharic language & Word2 Vec and TF-IDF & NB and RF: 79.83\% \& 65.34\% \\ \hline [PERSON] et al. [29] & Detection of HS in the Indonesian Language & Bag-of-word (BOW), word n-gram and character n-gram & NB, SVM, Bayesian LR (BLR) \& \\ \hline [PERSON] et al. [30] & Detection of HS with comment embeddings & Paragraph2 vec \& BOW with TF \& TF-IDF \\ \hline [PERSON] et al. [31] & Detection of HS on Twitter & Unigrams with sentimental, semantic features and pattern features & J4 Sgraft, SVM and RF: accuracy 87.4\% for binary and 78.4\% for ternary \\ \hline [PERSON] et al. [32] & Ensemble method for HS detection in Indonesian Twitter & BOW with TF-IDF weighting & NB, KNN, Maximum Entropy, RF, and SVM, and two ensemble methods. hard and soft vote, F1 measure 79.8\%. (SVM, NB, and RF) \\ \hline [PERSON] et al. [33] & Detection of HS in Kenyan tweets & Sentiment analysis \& N-Gram feature & NB: P-0.58, R-0.62, A-0.67 \\ \hline \multicolumn{4}{c}{**Multi-class hate speech**} \\ \hline [PERSON] et al. [24] & Automated HS and offensive language detection & Bigram, unigram, and trigram features with TF-IDF & Logistic regression with L1 regularization: 90\% \\ \hline [PERSON] et al. [34] & Racism detection in Dutch social media & Word2 vec, Dictionary-based SVM; F1: 0.46 \\ \hline [PERSON] et al. [35] & Detecting HS and offensive language on Twitter & N-gram and TF-IDF & LR, NB and SVM 95.6\% \\ \hline [PERSON] and [PERSON] [13] & Detecting HS in social media & Word skip-gram, and surface n-gram & SVM: 0.78 \\ \hline \multicolumn{4}{c}{**Binary and multi-classification with deep learning**} \\ \hline [PERSON] and [PERSON] [25] & Convolutional neural network (CNN) to classify HS & Word2 vec, Random vector, character n-grams, and word2 vec+character n-grams & CNN with word2 vec: 0.78 F-score multi-classification \\ \hline [PERSON] and [PERSON] (2018) [36] & HS detection using NLP & Word2 vec with 300 dimensions & CNN accuracy of 91\%, and a loss of 36\%. \\ \hline [PERSON] et al. [37] & HS detection using deep learning & Random embeddings \& glove embeddings & CNN, LTSM \& Fast Text best accuracy is 93\% f-score CNN + Random \& Glove Embeddings \\ \hline \multicolumn{4}{c}{**Rule-based hate speech detection**} \\ \hline [PERSON] et al. (2015) [38] & A lexicon-based approach for hate speech detection & Semantic features, subjectivity features, and grammatical patterns features & Subjectivity rule-based classifier called Subjective lexicon F1-score 65.12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of hate speech detection related work. policy does not allow access to the public contents of a private page. Table 2 lists the Facebook pages that this paper used for dataset building. The sampling criteria and metrics used in this paper for selecting public pages on social media platforms are listed below:  
* The number of followers and likes has to be greater than 50,000, because such criteria allows for more active public pages to be included in categories;
* Pages that post news or hot issues on political, ethnicity, religious, or gender issues at least once every two days;
* Pages that use the Odia language frequently for posts and comments;
* Pages that published more than 500 posts from April 2018 to April 2019. This paper collects posts and comments from public pages that are in the listed categories with larger number of followers and likes than the other pages in the category.