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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\u2019s predictions, enhancing understanding of its decision-making process.<\/jats:p>","DOI":"10.1007\/s40747-024-01655-1","type":"journal-article","created":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T05:11:31Z","timestamp":1731647491000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Enhancing misogyny detection in bilingual texts using explainable AI and multilingual fine-tuned transformers"],"prefix":"10.1007","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2526-9899","authenticated-orcid":false,"given":"Ehtesham","family":"Hashmi","sequence":"first","affiliation":[]},{"given":"Sule Yildirim","family":"Yayilgan","sequence":"additional","affiliation":[]},{"given":"Muhammad Mudassar","family":"Yamin","sequence":"additional","affiliation":[]},{"given":"Mohib","family":"Ullah","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,15]]},"reference":[{"issue":"7","key":"1655_CR1","first-page":"3629","volume":"14","author":"S Akuma","year":"2022","unstructured":"Akuma S, Lubem T, Adom IT (2022) Comparing bag of words and tf-idf with different models for hate speech detection from live tweets. 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