Reflection of Political Bias within YouTube Search and Recommendation Algorithms

Given the reach of YouTube as a proliferator of contemporary news and ideas, it is important to understand how YouTube reflects (either magnifies, minimizes, or preserves) pre-existing political bias. Moreover, it remains imperative to examine whether YouTube’s reflection of political bias disproportionately favors distinct political groups and whether it encourages the confirmation of biases. Previous research has examined user data and ideological homogeneity within social media groups, however, the extent of YouTube’s reflection of its user base’s political bias remains relatively unexplored. Principally, our research aims to provide a novel understanding of YouTube’s reflection of user base political bias within its search and video recommendation algorithms. Thus, we created two experiments to understand each of the aforementioned systems. Experiment 1 examines the relationship between videos’ political bias values, measured by applying an optimized BERT NLP regression model to video transcripts, to their respective page orders. This experiment scraped the top 200 search results over the span of 30 politically charged terms with four distinct political profiles (left, right, combination, null). Experiment 2 examines the progression of bias when repeatedly clicking the “Up-Next” recommended video per each video cycle. Our second experiment examined 1200  “seed” videos from our first experiment with 200 videos per category (extreme-right, moderate-right, minimal-right, minimal-left, moderate-left, extreme-left), examining 10 subsequent click cycles per seed video. Our first experiment finds that YouTube disproportionately ranks left-leaning videos above right-leaning videos within the top 3 search results. Our second experiment finds that YouTube actually minimizes the magnitude of bias within subsequent cycles of video recommendations. However, we also find that YouTube has a greater minimization effect with right-leaning minimally biased seed videos than its left-leaning counterparts and a greater minimization effect with left-leaning extremely biased videos than their right-leaning counterparts. Ultimately, our results provide a nuanced understanding of YouTube's reflection of political bias and have vast potential implications.