The online trend of the manosphere and feminist discourse on social networks
requires a holistic measure of the level of sexism in an online community. This
indicator is important for policymakers and moderators of online communities
(e.g., subreddits) and computational social scientists, either to revise
moderation strategies based on the degree of sexism or to match and compare the
temporal sexism across different platforms and communities with real-time
events and infer social scientific insights.
In this paper, we build a model that can provide a comparable holistic
indicator of toxicity targeted toward male and female identity and male and
female individuals. Despite previous supervised NLP methods that require
annotation of toxic comments at the target level (e.g. annotating comments that
are specifically toxic toward women) to detect targeted toxic comments, our
indicator uses supervised NLP to detect the presence of toxicity and
unsupervised word embedding association test to detect the target
automatically.
We apply our model to gender discourse communities (e.g., r/TheRedPill,
r/MGTOW, r/FemaleDatingStrategy) to detect the level of toxicity toward genders
(i.e., sexism). Our results show that our framework accurately and consistently
(93% correlation) measures the level of sexism in a community. We finally
discuss how our framework can be generalized in the future to measure qualities
other than toxicity (e.g. sentiment, humor) toward general-purpose targets and
turn into an indicator of different sorts of polarizations.

使用监督自然语言处理方法和无监督词嵌入关联测试，本研究构建了一个可以提供针对男性和女性特征以及男性和女性个体的全面性中毒性指标的模型，应用于性别话语社区，并准确地测量了社区中性别歧视的水平。