Intertwined Biases Across Social Media Spheres: Unpacking Correlations in Media Bias Dimensions

Yifan Liu, Yike Li, Dong Wang

Abstract: Biased information on social media significantly influences public perception by reinforcing stereotypes and deepening societal divisions. Previous research has often isolated specific bias dimensions, such as political or racial bias, without considering their interrelationships across different domains. The dynamic nature of social media, with its shifting user behaviors and trends, further challenges the efficacy of existing benchmarks. Addressing these gaps, our research introduces a novel dataset derived from five years of YouTube comments, annotated for a wide range of biases including gender, race, politics, and hate speech. This dataset covers diverse areas such as politics, sports, healthcare, education, and entertainment, revealing complex bias interplays. Through detailed statistical analysis, we identify distinct bias expression patterns and intra-domain correlations, setting the stage for developing systems that detect multiple biases concurrently. Our work enhances media bias identification and contributes to the creation of tools for fairer social media consumption.

External IDs:dblp:conf/asunam/LiuLW24