## 2 Related Work
### Toxic Speech Detection in English Text  
The literature includes several proposed approaches to detecting toxic content in the English text. Researchers have identified English toxic text by detecting hate tweets [15; 10; 11; 16; 17; 18; 19], offensive tweets [20; 21; 22], and a mix of both [23; 24; 25]. Various algorithms were proposed for detecting toxic content in English text, namely machine learning (ML)-based classification algorithms [15; 16; 20; 22; 23; 24] and deep learning (DL)-based classification algorithms [17; 21; 11; 25]. [PERSON] and [PERSON] [10] built a bag-of-words naive Bayes classifier model to predict English hate tweets targeting the Black community. The model resulted in an accuracy of 76%. [PERSON] and [PERSON] found out that 86% of the racist tweets were categorized as racist only because they contained offensive words. Hence, by considering only the unigram features of the tweets, the model was not sufficiently accurate since the presence of offensive words in a non-hate tweet can also lead to the wrong classification of tweets as anti-Black. Hate speech needs to be carefully understood since it can even be expressed subtly without offensive words. [PERSON] and [PERSON] [20] used critical race theory and built a logistic regression classifier that considers additional features to tweets, primarily demographic and geographic data. Even though demographic features did help enhance the accuracy of prediction, this type of information is usually not directly available or reliable on Twitter. [PERSON] et al. [16] proposed an ML-based classification algorithm to detect racist tweets in English that use unique hate code words referencing specific communities to avoid violating social media policies. A support vector machine (SVM) classifier was built to classify the racist tweets that used hate code words intentionally and the non-hate tweets that used the code words in a regular context. The model achieved an accuracy of 79.4%. The model was further used to identify the user handles who posted hateful tweets using code words and the frequency of tweets above a threshold value. These results helped the model understand the usage patterns of such users showing racism. [PERSON] et al. [23] proposed an ML-based classification algorithm to classify hate speech, offensive language, or neither in English tweets. Logistic regression with L2 regularization was used as a multi-class classification model, which achieved a precision of 91%, recall of 0.90%, and F1-score of 90%. Predictions, accuracies, and errors were closely analyzed to understand the possibilities and difficulties in accurately classifying hate speech, offensive language, and neither. Homophobic and racist tweets were mainly classified as hateful, sexist tweets were classified as offensive, and the tweets without any hateful or offensive terms were difficult to be classified. [PERSON] et al. [24] used features such as unigrams, patterns, sentiments, and semantics to detect hate speech and offensive English language. The J48 graft algorithm was built to perform classification using the WEKA toolkit. An accuracy of 87.4% was achieved for binary classification of tweets (offensive or clean) and 78.4% for ternary classification (hateful, offensive, or clean) using all the features combined. [PERSON] et al. [26] proposed ML and DL-based classification algorithms to classify offensive content on Twitter and further identify the type and target of these offences. SVM, Bi-directional LSTM (BiLSTM), and CNN models were built, and their performances were compared using a macro-averaged F1-score. The CNN model performed best in all three stages: detecting offensive tweets, categorizing the type of offense, and identifying the target. [PERSON] and [PERSON] [22] proposed ML-based classification algorithms to detect offensive language in tweets and enhance the performance through the quality of features and data configuration.