Gender Bias in Fake News: An Analysis
This work addresses gender bias in fake news for researchers and practitioners, highlighting it as a critical consideration, though it is incremental as it applies existing methods to a new domain.
The paper tackled the problem of gender bias in fake news by conducting the first empirical analysis using lexicon-based methods on public datasets, finding increased prevalence of gender bias across abundance, affect, and proximal words.
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.