Gender Bias in YouTube Exposure: Allocative and Structural Inequalities in Political Information Environments
For researchers and policymakers concerned with algorithmic fairness on digital platforms, this work provides empirical evidence of gender-based inequalities in political information environments.
This paper investigates gender bias in YouTube's recommendation algorithm by conducting a controlled social-bot field experiment with male-coded and female-coded profiles. It finds statistically significant allocative and structural biases in political information exposure, with differences in issue distribution, ideological orientation, and clustering patterns, and reproduces these biases with a collaborative-filtering model.
Recommendation algorithms have become the dominant mechanism for information distribution on digital platforms, profoundly shaping personalized information consumption environments. However, gender bias, as a significant form of algorithmic discrimination, may cause users to experience unequal exposure within different political information environments. Taking YouTube as a case, we conduct a controlled social-bot field experiment, where male-coded and female-coded profiles are constructed. We track the exposure and click patterns of these bots to analyze their recommendation trajectories. We analyze the distribution of recommended content from two dimensions: allocative bias and structural bias. First, we find statistically significant differences in allocative bias across male-coded and female-coded profiles, particularly in terms of issue distribution, ideological orientation, and political entities. Secondly, we observe structural bias in the political information environments, characterized by distinct clustering patterns. Additionally, time-series analysis shows that exposure pathways continue to be shaped over time by both communities detected in the co-occurrence network and individual profile-level dynamics. Finally, we construct a simple collaborative-filtering model that reproduces the observed gender bias. We argue that gender bias in recommendation systems is reflected not only in the allocation of political content, but also in how community structures shape these environments, reinforcing societal inequalities and highlighting the need for algorithmic fairness.