一种衡量网络性别歧视的整体极化指标
A Holistic Indicator of Polarization to Measure Online Sexism
AI总结：
本文提出一种结合有监督毒性检测与无监督词嵌入关联测试的整体极化指标，用于自动衡量在线社区中的性别歧视水平，并在多个性别话语社区中验证了其准确性和一致性。
摘要：
社交网络上男性圈（manosphere）与女权主义话语的在线趋势，要求对在线社区中的性别歧视程度进行整体衡量。该指标对政策制定者、在线社区（如 subreddits）的版主以及计算社会科学家都很重要，既可以用于根据性别歧视程度修订管理策略，也可以用于跨平台、跨社区匹配和比较随时间变化的性别歧视与实时事件，并推断社会科学洞见。本文构建了一个模型，能够提供可比较的整体毒性指标，该指标针对男性和女性身份以及男性和女性个体。以往的有监督自然语言处理方法需要在目标层面标注有毒评论（例如标注专门针对女性的有毒评论）才能检测有针对性的有毒评论，而我们的指标使用有监督自然语言处理来检测毒性的存在，并使用无监督词嵌入关联测试来自动检测目标。我们将模型应用于性别话语社区（如 r/TheRedPill、r/MGTOW、r/FemaleDatingStrategy），以检测针对不同性别的毒性水平（即性别歧视）。结果表明，我们的框架能够准确且一致地（93% 相关性）衡量社区中的性别歧视水平。最后，我们讨论了该框架未来如何推广，以衡量面向通用目标的其他性质（如情感、幽默），并转化为不同类型极化的指标。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
