Enhancing misogyny detection in bilingual texts using explainable AI and multilingual fine-tuned transformers
Abstract Gendered disinformation undermines women’s rights, democratic principles, and national security by worsening societal divisions through authoritarian regimes’ intentional weaponization of social media. Online misogyny represents a harmful societal issue, threatening to transform digital pla...
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| Language: | English |
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Springer
2024-11-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01655-1 |
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| author | Ehtesham Hashmi Sule Yildirim Yayilgan Muhammad Mudassar Yamin Mohib Ullah |
| author_facet | Ehtesham Hashmi Sule Yildirim Yayilgan Muhammad Mudassar Yamin Mohib Ullah |
| author_sort | Ehtesham Hashmi |
| collection | DOAJ |
| description | Abstract Gendered disinformation undermines women’s rights, democratic principles, and national security by worsening societal divisions through authoritarian regimes’ intentional weaponization of social media. Online misogyny represents a harmful societal issue, threatening to transform digital platforms into environments that are hostile and inhospitable to women. Despite the severity of this issue, efforts to persuade digital platforms to strengthen their protections against gendered disinformation are frequently ignored, highlighting the difficult task of countering online misogyny in the face of commercial interests. This growing concern underscores the need for effective measures to create safer online spaces, where respect and equality prevail, ensuring that women can participate fully and freely without the fear of harassment or discrimination. This study addresses the challenge of detecting misogynous content in bilingual (English and Italian) online communications. Utilizing FastText word embeddings and explainable artificial intelligence techniques, we introduce a model that enhances both the interpretability and accuracy in detecting misogynistic language. To conduct an in-depth analysis, we implemented a range of experiments encompassing classic machine learning methodologies and conventional deep learning approaches to the recent transformer-based models incorporating both language-specific and multilingual capabilities. This paper enhances the methodologies for detecting misogyny by incorporating incremental learning for cutting-edge datasets containing tweets and posts from different sources like Facebook, Twitter, and Reddit, with our proposed approach outperforming these datasets in metrics such as accuracy, F1-score, precision, and recall. This process involved refining hyperparameters, employing optimization techniques, and utilizing generative configurations. By implementing Local Interpretable Model-agnostic Explanations (LIME), we further elucidate the rationale behind the model’s predictions, enhancing understanding of its decision-making process. |
| format | Article |
| id | doaj-art-1cc363b9553642bd9f606514359a15f6 |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-1cc363b9553642bd9f606514359a15f62025-02-02T12:49:27ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-11-0111111910.1007/s40747-024-01655-1Enhancing misogyny detection in bilingual texts using explainable AI and multilingual fine-tuned transformersEhtesham Hashmi0Sule Yildirim Yayilgan1Muhammad Mudassar Yamin2Mohib Ullah3Department of Information Security and Communication Technology (IIK), Norwegian University of Science and Technology (NTNU)Department of Information Security and Communication Technology (IIK), Norwegian University of Science and Technology (NTNU)Department of Information Security and Communication Technology (IIK), Norwegian University of Science and Technology (NTNU)Intelligent Systems and Analytics (ISA) Research Group, Department of Computer Science (IDI), Norwegian University of Science and Technology (NTNU)Abstract Gendered disinformation undermines women’s rights, democratic principles, and national security by worsening societal divisions through authoritarian regimes’ intentional weaponization of social media. Online misogyny represents a harmful societal issue, threatening to transform digital platforms into environments that are hostile and inhospitable to women. Despite the severity of this issue, efforts to persuade digital platforms to strengthen their protections against gendered disinformation are frequently ignored, highlighting the difficult task of countering online misogyny in the face of commercial interests. This growing concern underscores the need for effective measures to create safer online spaces, where respect and equality prevail, ensuring that women can participate fully and freely without the fear of harassment or discrimination. This study addresses the challenge of detecting misogynous content in bilingual (English and Italian) online communications. Utilizing FastText word embeddings and explainable artificial intelligence techniques, we introduce a model that enhances both the interpretability and accuracy in detecting misogynistic language. To conduct an in-depth analysis, we implemented a range of experiments encompassing classic machine learning methodologies and conventional deep learning approaches to the recent transformer-based models incorporating both language-specific and multilingual capabilities. This paper enhances the methodologies for detecting misogyny by incorporating incremental learning for cutting-edge datasets containing tweets and posts from different sources like Facebook, Twitter, and Reddit, with our proposed approach outperforming these datasets in metrics such as accuracy, F1-score, precision, and recall. This process involved refining hyperparameters, employing optimization techniques, and utilizing generative configurations. By implementing Local Interpretable Model-agnostic Explanations (LIME), we further elucidate the rationale behind the model’s predictions, enhancing understanding of its decision-making process.https://doi.org/10.1007/s40747-024-01655-1MisogynyFastTextMachine LearningDeep LearningTransformersExplainable AI |
| spellingShingle | Ehtesham Hashmi Sule Yildirim Yayilgan Muhammad Mudassar Yamin Mohib Ullah Enhancing misogyny detection in bilingual texts using explainable AI and multilingual fine-tuned transformers Complex & Intelligent Systems Misogyny FastText Machine Learning Deep Learning Transformers Explainable AI |
| title | Enhancing misogyny detection in bilingual texts using explainable AI and multilingual fine-tuned transformers |
| title_full | Enhancing misogyny detection in bilingual texts using explainable AI and multilingual fine-tuned transformers |
| title_fullStr | Enhancing misogyny detection in bilingual texts using explainable AI and multilingual fine-tuned transformers |
| title_full_unstemmed | Enhancing misogyny detection in bilingual texts using explainable AI and multilingual fine-tuned transformers |
| title_short | Enhancing misogyny detection in bilingual texts using explainable AI and multilingual fine-tuned transformers |
| title_sort | enhancing misogyny detection in bilingual texts using explainable ai and multilingual fine tuned transformers |
| topic | Misogyny FastText Machine Learning Deep Learning Transformers Explainable AI |
| url | https://doi.org/10.1007/s40747-024-01655-1 |
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