仇恨的未见目标——仇恨言论数据集的系统性综述
The Unseen Targets of Hate -- A Systematic Review of Hateful Communication Datasets
AI总结：
本文系统综述近十年仇恨言论自动检测数据集，揭示目标身份代表性偏差及概念化与实际纳入目标不匹配的问题，并指出研究范围正趋于拓宽和多元。
摘要：
基于机器学习（ML）的内容审核工具对于保持网络空间免受仇恨言论侵害至关重要。然而，ML工具的能力只能达到其训练数据质量所允许的水平。越来越多的证据表明，它们在检测针对特定身份群体的仇恨言论时表现不佳，并可能对这些群体产生歧视，但令人惊讶的是，我们对这种偏见的来源知之甚少。为填补这一空白，我们对过去十年间提出的用于自动检测仇恨言论的数据集进行了系统性综述，并根据数据集所体现的身份来剖析其质量：这些身份既包括数据整理者重点关注的仇恨言论目标，也包括数据集中无意包含的身份。总体而言，我们发现所选目标身份的代表性存在偏差，并且研究在概念层面所界定的目标与最终纳入数据集的目标之间存在不匹配。不过，通过将这些发现与数据集的源语言和来源地区相结合进行语境化分析，我们强调该研究领域正呈现出向拓宽和多元化发展的积极趋势。
英文摘要：
Machine learning (ML)-based content moderation tools are essential to keep online spaces free from hateful communication. Yet, ML tools can only be as capable as the quality of the data they are trained on allows them. While there is increasing evidence that they underperform in detecting hateful communications directed towards specific identities and may discriminate against them, we know surprisingly little about the provenance of such bias. To fill this gap, we present a systematic review of the datasets for the automated detection of hateful communication introduced over the past decade, and unpack the quality of the datasets in terms of the identities that they embody: those of the targets of hateful communication that the data curators focused on, as well as those unintentionally included in the datasets. We find, overall, a skewed representation of selected target identities and mismatches between the targets that research conceptualizes and ultimately includes in datasets. Yet, by contextualizing these findings in the language and location of origin of the datasets, we highlight a positive trend towards the broadening and diversification of this research space.
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。
