Discovering and Categorising Language Biases in Reddit
Xavier Ferrer+, Tom van Nuenen+, Jose M. Such+ and Natalia Criado+ + Department of Informatics, King’s College London {xavier.ferrer_aran, tom.van_nuenen, jose.such,
Abstract
We present a data-driven approach using word embeddings to discover and categorise language biases on the discussion platform Reddit. As spaces for isolated user communities, platforms such as Reddit are increasingly connected to issues of racism, sexism and other forms of discrimination. Hence, there is a need to monitor the language of these groups. One of the most promising AI approaches to trace linguistic biases in large textual datasets involves word embeddings, which transform text into high-dimensional dense vectors and capture semantic relations between words. Yet, previous studies require predefined sets of potential biases to study, e.g., whether gender is more or less associated with particular types of jobs. This makes these approaches unfit to deal with smaller and community-centric datasets such as those on Reddit, which contain smaller vocabularies and slang, as well as biases that may be particular to that community. This paper proposes a data-driven approach to automatically discover language biases encoded in the vocabulary of online discourse communities on Reddit. In our approach, protected attributes are connected to evaluative words found in the data, which a
中文速览
词嵌入（word embeddings）技术此前虽能在大型语料库中识别性别、种族等偏见，却依赖预先设定的评估词表，难以适应Reddit这类规模较小、词汇独特的社群话语。研究者提出了一套数据驱动的自动发现框架：先用词嵌入模型计算词汇表中每个词对"受保护属性"（如男性/女性）目标词集的偏向分数并排序，再结合情感极性分析与K-means聚类对偏见词进行语义分类，从而在无需预定义偏见类型的情况下揭示社群特有的歧视性语言。研究团队先在谷歌新闻数据集上验证了方法的有效性，随后将其应用于/r/TheRedPill、/r/dating_advice等多个Reddit社区，成功发现并归类了性别偏见、宗教偏见和族裔偏见。这项工作的价值在于，它让偏见监测能够深入那些拥有专属俚语和亚文化逻辑的封闭网络社群，为追踪网络仇恨言论和有害意识形态提供了更具普适性的工具。
原文 arXiv:2008.02754;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2008.02754v2