  Online sexism has become an increasing concern in social media platforms as
it has affected the healthy development of the Internet and can have negative
effects in society. While research in the sexism detection domain is growing,
most of this research focuses on English as the language and on Twitter as the
platform. Our objective here is to broaden the scope of this research by
considering the Chinese language on Sina Weibo. We propose the first Chinese
sexism dataset -- Sina Weibo Sexism Review (SWSR) dataset --, as well as a
large Chinese lexicon SexHateLex made of abusive and gender-related terms. We
introduce our data collection and annotation process, and provide an
exploratory analysis of the dataset characteristics to validate its quality and
to show how sexism is manifested in Chinese. The SWSR dataset provides labels
at different levels of granularity including (i) sexism or non-sexism, (ii)
sexism category and (iii) target type, which can be exploited, among others,
for building computational methods to identify and investigate finer-grained
gender-related abusive language. We conduct experiments for the three sexism
classification tasks making use of state-of-the-art machine learning models.
Our results show competitive performance, providing a benchmark for sexism
detection in the Chinese language, as well as an error analysis highlighting
open challenges needing more research in Chinese NLP. The SWSR dataset and
SexHateLex lexicon are publicly available.
