Gender Bias in Fake News: An Analysis

TL;DR

This paper empirically investigates the relationship between gender bias and fake news, revealing that gender bias is more prevalent in fake news across various linguistic facets, highlighting its importance in fake news research.

Contribution

It provides the first empirical analysis linking gender bias with fake news using transparent lexicon-based methods on benchmark datasets.

Findings

Gender bias is more prevalent in fake news across multiple facets.

Fake news contains more affect and proximal words related to gender bias.

Gender bias should be a key consideration in fake news research.

Abstract

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.