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.

性别化的虚假信息通过专制政权故意将社交媒体武器化，加剧了社会分化，从而破坏了妇女权利、民主原则和国家安全。在线厌女症代表着一个有害的社会问题，有可能将数字平台转变为对女性充满敌意和不友好的环境。尽管这个问题很严重，但说服数字平台加强对性别虚假信息的保护的努力经常被忽视，这凸显了在商业利益面前打击在线厌女症的艰巨任务。这种日益增长的担忧凸显了采取有效措施创造更安全的在线空间的必要性，尊重和平等盛行，确保女性能够充分自由地参与，而不必担心受到骚扰或歧视。本研究解决了在双语（英语和意大利语）在线通信中检测厌女内容的挑战。利用 FastText 单词嵌入和可解释的人工智能技术，我们引入了一个模型，可以提高检测厌女语言的可解释性和准确性。为了进行深入分析，我们实施了一系列实验，包括经典的机器学习方法和传统的深度学习方法，以及最近基于 transformer 的模型，这些模型结合了特定于语言和多语言的功能。本文通过对包含来自不同来源（如 Facebook、Twitter 和 Reddit）的推文和帖子的尖端数据集进行增量学习，增强了检测厌女症的方法，我们提出的方法在准确性、F1 分数、精度和召回率等指标上优于这些数据集。 这个过程包括优化超参数、采用优化技术和利用生成配置。通过实施局部可解释模型不可知解释 （LIME），我们进一步阐明了模型预测背后的基本原理，增强了对其决策过程的理解。