## 3 Description of the Dataset  
This study requires a dataset that consists of a mix of English tweets and Hindi-English (code-mixed) tweets. Moreover, the tweets must be classified/labeled as hate or non-hate. Unfortunately, we could not find a publicly available dataset with such specifications. To ensure the repeatability of the experiments, we opted to employ widely used datasets in the literature by combining existing datasets created and used by pertinent research work [12; 21; 23]. Our dataset is formed by collecting tweets from three different sources, as follows:  
1. The first portion of our dataset is collected from the dataset used by [PERSON] et al. [23]. [PERSON] et al.'s dataset contains tweets written in the English language and labels tweets as \"hate\", \"offensive\", or \"neither\". However, for the sake of integrating this dataset into our work (i.e., keeping only two class labels), we relabeled the \"offensive\" class as hate and \"neither\" as non-hate. As a result, we had a dataset containing 24,783 tweets in English, where 4163 tweets were classified as non-hate and 20,620 as hate.
2. The second portion of our dataset is collected from the dataset used by [PERSON] et al. [12]. This dataset contains tweets written in Hindi-English code-mixed language and tweets labeled as \"hate\" or \"non-hate\". Overall, this dataset contains 4579 tweets, where 2918 of them are classified as non-hate and 1661 as hate.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Study By** & **Model Used** & **English** & **Hindi–English** & **Multilingual** \\ \hline [PERSON] and [PERSON] [10] & & Naive Bayes & & \\ [PERSON] and [PERSON] [20] & & Logistic regression & & \\ [PERSON] et al. [16] & & SVM & & \\ [PERSON] et al. [23] & & Logistic regression & & \\ [PERSON] et al. [24] & & Decision Tree & & \\ [PERSON] et al. [26] & & SVM, BiLSTM, CNN & & \\ [PERSON] and [PERSON] [22] & & Linear SVM, naive Bayes & & \\ \hline [PERSON] et al. [12] & & SVM, random forest & & \\ [PERSON] and [PERSON] [29] & & SVM, random forest, LSTM, BiLSTM & & \\  & Naïve Bayes, decision tree, SVM, multilayer & & \\ [PERSON] et al. [13] & & perceptron, LSTM, CNN & & \\ [PERSON] and [PERSON] [8] & & SVM, random forest, LSTM & & \\ \hline [PERSON] et al. [33] & & LSTM & & \\ \hline [PERSON] et al. [7] & & CNN & ✓ & ✓ \\ \hline Our proposed work & CNN, LSTM & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of comparison of related work.  
3. The third portion of our dataset is collected from the dataset used by [PERSON] et al. [21]. [PERSON] et al.'s dataset contains tweets written in Hindi-English code-mixed language tweets labeled as \"hate-inducing\", \"non-offensive\", or \"abusive\". For the sake of having two class labels, we relabeled \"hate-inducing\" and \"abusive\" as hate and relabeled \"non-offensive\" as non-hate. In total, this dataset contains 3189 tweets in code-mixed Hindi-English, where 1121 of them are classified as non-hate and 2068 as hate.  
The final combined dataset has two columns: \"Tweet\" (text content) and \"Label\" (hate or non-hate). This resulting dataset consists of a total of 32,551 tweets, where 8202 are classified as non-hate and 24,349 as hate. Table 2 summarizes the final dataset and its class labels.  
Formally, a tweet, t, is a combination of characters. The maximum size of t, denoted by \(|t|\), is 280 characters. A class label c \(\in\) {\"hate\", \"non-hate\"}. A dataset record r = \(<\)t, c\(>\) is an ordered set, where t is the tweet and c is t's associated class label. The dataset, D, is a set of records, i.e., D = \(\{r_{1},r_{2},\ldots,r_{n}\}\), where n = \(|\)D\(|\) = the total number of records in the D.