Sexism Detection
Detection and Quantification of Feminist and Sexist Content on Social Media
During the last decade, hateful and sexist content towards women is being increasingly spread on social networks. Exposure to sexist speech has serious consequences for women’s life and limits their freedom of speech. Previous studies have focused on identifying hatred or violence towards women. However, sexism is expressed in very different forms: it includes subtle stereotypes and attitudes that, although frequently unnoticed, are extremely harmful to both women and society. In this work, we experiment with a new task that aims to understand and analyze how sexism, from explicit hate or violence to subtle expressions, is expressed in online conversations.
Keywords: : Python, Natural Language Processing, Sentiment Analysis, Data Scraping, Data Analysis, BERT, DistilBERT, RoBERTA
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