# Threatening URDU Language Detection from Tweets Using Machine Learning
## 2 Literature Review  
This section discusses existing studies for threatening language detection for the Urdu language. To the best of the authors' knowledge, there is only one significant study for threatening language detection in the Urdu language. [PERSON] et al. [12] have contributed an Urdu corpus b for threatening Urdu language detection. In addition, the authors performed experiments using machine learning-based algorithms such as LR, multi-layer perceptron (MLP), AdaBoost, and deep learning methods for the detection of threatening language. Many existing studies focus on different languages like English, Arabic, Spanish, and others. In the English language, many studies focused on inappropriate and abusive language detection. For example, ref. [13] present an automatic flame detection, which can be rants, taunts, and squalid phrases in the English language. The authors used a multi-level classification paradigm where complement Naive Bayes (NB) is utilized. For the second level, multinomial updatable NB is selected, while the last level chose a decision table and NB-based hybrid classifier. The study [14] proposed the use of lexical syntactic features for offensive language detection on social media platforms. The study specifically focused on name-callingharassment by studying the user's writing style and structure. Results demonstrate that the proposed approach can obtain 98.24% for offensive sentence detection and performs better than existing methods. Similarly, ref. [15] focused on detecting different kinds of offensive content on social media platforms. The data is annotated using a three-layer scheme. Experiments are performed using a support vector machine (SVM), bi-directional LSTM, and CNN. A 0.80 macro F1 is reported using the CNN model. [PERSON] et al. [16] proposed a semi-supervised approach for offensive language detection on Twitter. Statistical topic modeling is used to exploit linguistic regularities for this purpose. Results show a 75.1% true positive rate using the LR model. Similarly, hateful and antagonistic content is identified in [17] employing the K-means algorithm. The study focuses on text containing more than one characteristic like race and sexual orientation. Machine learning models using the BoW approach provided an average precision of 87.83% for six classes of hateful content. [PERSON] et al. [18] employed NB and SVM with hateword2 vec, and hatedoc2 vec with n-grams for aggressive text detection from tweets. In addition, the synthetic minority oversampling technique (SMOTE) is applied for data oversampling. An F1 score of 42.85% is obtained using SMOTE for the testing dataset. The study [19] uses graph convolutional networks (GCN) to obtain deep properties by modeling follower-following relationships. Using LR with GCN, a precision of 86.23% is obtained while recall and F1 scores are 84.73% and 85.42%, respectively. Besides the English language, abusive content detection from other languages is also studied by several researchers. For example, ref. [20] proposes a method to detect nasty comments from Japanese posts from bulletin board systems. The study employs SVM with an n-gram model and obtains a 72.25% F1 score. The study [21] explored n-grams up to 8-grams to detect the offensive language in English. Three systems are developed for three sub-tasks in offensive text detection. The proposed approach achieves 79.76%, 87.91%, and 44.37% accuracy scores, respectively. [PERSON] et al. [22] used ALBERTO with BERT tokens for hate speech detection in Italian detection. The model is based on Italian language understanding and is trained with BERT and 200M Italian tweets. Results show an average F1 score of 0.8410 for the model. The hate speech dataset and detection system for the Turkish language are presented in [23]. Tweets collected for experiments are from two different domains. The study proposed BERTurk, which is a transformer architecture. Experimental results using five-fold cross-validation indicate an accuracy of 77%.