An Expert Annotated Dataset for the Detection of Online Misogyny
An Expert Annotated Dataset for the Detection of Online Misogyny
复制标题
用于检测在线厌女症的专家注释数据集
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
H. Margetts
中科院分区：
文献类型：
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作者：
E. Guest;Bertie Vidgen;Alexandros Mittos;Nishanth R. Sastry;Gareth Tyson;H. Margetts
网络厌女症是一个有害的社会问题，有可能使网络平台变得有毒，不受女性欢迎。我们为在线厌女症提出了一个新的分层分类法，以及一个专家标记数据集，以实现对厌女症内容的自动分类。该数据集由6567个Reddit帖子和评论标签组成。正如之前的研究发现，未经训练的众包注释者很难识别厌女症，我们雇佣并培训了注释者，并为他们提供了健全的注释指南。我们报告了在二元分类任务上的基线分类性能，达到了0.93的准确率和0.43的F1。代码本和数据集可供未来的研究人员免费使用。
Online misogyny is a pernicious social problem that risks making online platforms toxic and unwelcoming to women. We present a new hierarchical taxonomy for online misogyny, as well as an expert labelled dataset to enable automatic classification of misogynistic content. The dataset consists of 6567 labels for Reddit posts and comments. As previous research has found untrained crowdsourced annotators struggle with identifying misogyny, we hired and trained annotators and provided them with robust annotation guidelines. We report baseline classification performance on the binary classification task, achieving accuracy of 0.93 and F1 of 0.43. The codebook and datasets are made freely available for future researchers.