  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.
