Abstract

Misogyny is characterized by hostility, hatred, aversion, intimidation, and violence against women. With the rise of social media, it has become one of the most convenient platforms for expressing woman-hating speech. As a result, misogyny is gaining appeal and societal standards are being violated. With millions of Bangladeshi Facebook users, misogyny is growing increasingly prevalent in Bangla as well. In this paper, we have proposed automatically identifying misogynistic content in Bangla on social media platforms in order to evaluate the problem's challenges. As there is no existing Bangla dataset for analyzing misogynistic text, we generated our own. We have applied various deep-learning algorithms to improve the classification of misogynistic text categories. LSTM and RNN models are used for designing the model architecture in deep learning. Models are evaluated using the confusion matrix, accuracy, and f1-scores. The results indicate that LSTM outperforms RNN in terms of accuracy by 67

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Cite this article

@article{cai-6515,
  author  = {Sarif Sultan Saruar Jahan and Raqeebir Rab and Peom Dutta and Hossain Muhammad Mahdi Hassan Khan and Muhammad Shahariar Karim Badhon and Sumaiya Binte Hassan and Ashikur Rahman},
  title   = {Deep Learning Based Misogynistic Bangla Text Identification from Social Media},
  journal = {Computing and Informatics},
  volume  = {42},
  number  = {4},
  pages   = {993--1012},
  year    = {2023},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2023_4_993}
}
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