Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization

With the rapid increase of users over social media, cyberbullying, and hate speech problems have arisen over the past years. Automatic hate speech detection (HSD) from text is an emerging research problem in natural language processing (NLP). Researchers developed various approaches to solve the aut...

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Vydáno v:Scientific reports Ročník 14; číslo 1; s. 28590 - 22
Hlavní autoři: Ashiq, Waqar, Kanwal, Samra, Rafique, Adnan, Waqas, Muhammad, Khurshaid, Tahir, Montero, Elizabeth Caro, Alonso, Alicia Bustamante, Ashraf, Imran
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 19.11.2024
Nature Publishing Group
Nature Portfolio
Témata:
639/705/117
639/705/258
Accuracy
Algorithms
Bayes Theorem
Bayesian analysis
Benchmarks
Classification
Cyberbullying
Deep Learning
Hate
Hate speech
Hate speech detection
Humanities and Social Sciences
Humans
Language
Learning algorithms
Machine Learning
Mathematical models
Model optimization
multidisciplinary
Natural Language Processing
Optimization
Science
Science (multidisciplinary)
Social discrimination learning
Speech
Urdu text classification
Deep learning
Model optimization
Urdu text classification
Hate speech detection
ISSN:2045-2322, 2045-2322
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Popis
Shrnutí:With the rapid increase of users over social media, cyberbullying, and hate speech problems have arisen over the past years. Automatic hate speech detection (HSD) from text is an emerging research problem in natural language processing (NLP). Researchers developed various approaches to solve the automatic hate speech detection problem using different corpora in various languages, however, research on the Urdu language is rather scarce. This study aims to address the HSD task on Twitter using Roman Urdu text. The contribution of this research is the development of a hybrid model for Roman Urdu HSD, which has not been previously explored. The novel hybrid model integrates deep learning (DL) and transformer models for automatic feature extraction, combined with machine learning algorithms (MLAs) for classification. To further enhance model performance, we employ several hyperparameter optimization (HPO) techniques, including Grid Search (GS), Randomized Search (RS), and Bayesian Optimization with Gaussian Processes (BOGP). Evaluation is carried out on two publicly available benchmarks Roman Urdu corpora comprising HS-RU-20 corpus and RUHSOLD hate speech corpus. Results demonstrate that the Multilingual BERT (MBERT) feature learner, paired with a Support Vector Machine (SVM) classifier and optimized using RS, achieves state-of-the-art performance. On the HS-RU-20 corpus, this model attained an accuracy of 0.93 and an F1 score of 0.95 for the Neutral-Hostile classification task, and an accuracy of 0.89 with an F1 score of 0.88 for the Hate Speech-Offensive task. On the RUHSOLD corpus, the same model achieved an accuracy of 0.95 and an F1 score of 0.94 for the Coarse-grained task, alongside an accuracy of 0.87 and an F1 score of 0.84 for the Fine-grained task. These results demonstrate the effectiveness of our hybrid approach for Roman Urdu hate speech detection.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-79106-7