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.

翻译：近年来，数据科学对虚假新闻的研究取得了显著进展，这在很大程度上得益于大规模公共基准数据集的涌现。尽管媒体研究领域早已证实性别偏见是新闻媒体中普遍存在的问题，但关于性别偏见与虚假新闻之间关联性的探索却极为有限。本研究首次采用基于词典的简易透明方法，利用公共基准数据集对虚假新闻中的性别偏见进行了实证分析。我们的分析从三个维度——即频率、情感倾向和邻近词汇——证实了虚假新闻中性别偏见的显著增强。研究结果有力地证明，性别偏见应当成为虚假新闻研究中的一项重要考量因素。