论文标题
假新闻中的性别偏见：分析
Gender Bias in Fake News: An Analysis
论文作者
论文摘要
近年来，《虚假新闻》的数据科学研究已经筹集了很大的势头，可以说是大型公共基准数据集的出现。尽管在媒体研究中，性别偏见是一个遍布新闻媒体的问题，但对性别偏见与假新闻之间的关系几乎没有探索。在这项工作中，我们对假新闻的性别偏见进行了首次实证分析，利用公共基准数据集利用简单且基于透明的词典的方法。我们的分析确定了在三个方面的虚假新闻中性别偏见的普遍性，即丰富，情感和近端词。我们分析中的见解提供了一个有力的论点，即性别偏见需要成为对假新闻研究的重要考虑因素。
Data science research into fake news has gathered much momentum in recent years, arguably facilitated by the emergence of large public benchmark datasets. While it has been well-established within media studies that gender bias is an issue that pervades news media, there has been very little exploration into the relationship between gender bias and fake news. In this work, we provide the first empirical analysis of gender bias vis-a-vis fake news, leveraging simple and transparent lexicon-based methods over public benchmark datasets. Our analysis establishes the increased prevalance of gender bias in fake news across three facets viz., abundance, affect and proximal words. The insights from our analysis provide a strong argument that gender bias needs to be an important consideration in research into fake news.
