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Sexism is very common in social media and makes the boundaries of freedomtighter for feminist and female users. There is still no comprehensiveclassification of sexism attracting natural language processing techniques.Categorizing sexism in social media in the categories of hostile or benevolentsexism are so general that simply ignores the other types of sexism happeningin these media. This paper proposes a more comprehensive and in-depthcategories of online harassment in social media e.g. twitter into the followingcategories, "Indirect harassment", "Information threat", "sexual harassment","Physical harassment" and "Not sexist" and address the challenge of labelingthem along with presenting the classification result of the categories. It ispreliminary work applying machine learning to learn the concept of sexism anddistinguishes itself by looking at more precise categories of sexism in socialmedia.

When a Tweet is Actually Sexist. A more Comprehensive Classification of Different Online Harassment Categories and The Challenges in NLP