@article{uninimx4194,
           month = {Octubre},
           pages = {10342},
          number = {20},
            year = {2022},
         journal = {Applied Sciences},
          volume = {12},
          author = {Aneela Mehmood and Muhammad Shoaib Farooq and Ansar Naseem and Furqan Rustam and M{\'o}nica Gracia Villar and Carmen Lil{\'i} Rodr{\'i}guez Velasco and Imran Ashraf},
           title = {Threatening URDU Language Detection from Tweets Using Machine Learning},
             url = {http://repositorio.unini.edu.mx/id/eprint/4194/},
        abstract = {Technology?s expansion has contributed to the rise in popularity of social media platforms. Twitter is one of the leading social media platforms that people use to share their opinions. Such opinions, sometimes, may contain threatening text, deliberately or non-deliberately, which can be disturbing for other users. Consequently, the detection of threatening content on social media is an important task. Contrary to high-resource languages like English, Dutch, and others that have several such approaches, the low-resource Urdu language does not have such a luxury. Therefore, this study presents an intelligent threatening language detection for the Urdu language. A stacking model is proposed that uses an extra tree (ET) classifier and Bayes theorem-based Bernoulli Naive Bayes (BNB) as the based learners while logistic regression (LR) is employed as the meta learner. A performance analysis is carried out by deploying a support vector classifier, ET, LR, BNB, fully connected network, convolutional neural network, long short-term memory, and gated recurrent unit. Experimental results indicate that the stacked model performs better than both machine learning and deep learning models. With 74.01\% accuracy, 70.84\% precision, 75.65\% recall, and 73.99\% F1 score, the model outperforms the existing benchmark study.},
        keywords = {threatening language detection; Urdu text classification; machine learning; stacking}
}