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

TL;DR

This paper introduces a detailed classification system for online sexism on social media, addressing the limitations of broad categories and exploring machine learning methods to identify nuanced harassment types.

Contribution

It proposes a comprehensive set of sexism categories for social media and applies machine learning to improve classification accuracy, advancing NLP techniques in this domain.

Findings

Successful categorization of five distinct harassment types

Identification of challenges in labeling sexism categories

Preliminary machine learning results for sexism detection

Abstract

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.