Online Sexism Detection and Classification by Injecting User Gender Information
Amit Kumar Das, Mostafa Rahgouy, Zheng Zhang, Tathagata Bhattacharya, Gerry Vernon Dozier, Cheryl Seals · 2023
This paper investigates the potential effects that user gender information has on online sexism detection, in terms of both binary and multi class detection. Social media has recently developed into a center for sexist posts that target especially women. Since most sexist comments are made especially against people of a particular gender, whether the gender information of the users could be effective or not for sexism detection, is still an important question. Here we try to address this issue using Natural Language Processing (NLP) and machine learning models. Experiments showed that combining user gender information with textual features improved classification performance both in terms of binary classification and multi class classification. The effectiveness of the proposed strategy is demonstrated by the experimental results reported in this research.