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