Examining the Role of YouTube Production and Consumption Dynamics on the Formation of Extreme Ideologies

TL;DR

A longitudinal, mixed-methods analysis combining one year of YouTube watch history with two waves of ideological surveys from 1,100 U.S. participants identifies users who exhibited significant shifts toward more extreme ideologies and compares their content consumption and the production patterns of YouTube channels they engaged with to ideologically stable users.

Abstract

The relationship between content production and consumption on algorithm-driven platforms like YouTube plays a critical role in shaping ideological behaviors. While prior work has largely focused on user behavior and algorithmic recommendations, the interplay between what is produced and what gets consumed, and its role in ideological shifts remains understudied. In this paper, we present a longitudinal, mixed-methods analysis combining one year of YouTube watch history with two waves of ideological surveys from 1,100 U.S. participants. We identify users who exhibited significant shifts toward more extreme ideologies and compare their content consumption and the production patterns of YouTube channels they engaged with to ideologically stable users. Our findings show that users who became more extreme consumed have different consumption habits from those who do not. This gets amplified by the fact that channels favored by users with extreme ideologies also have a higher affinity to produce content with a higher anger, grievance and other such markers. Lastly, using time series analysis, we examine whether content producers are the primary drivers of consumption behavior or merely responding to user demand.

Figures (5)

• Figure 1: Distribution of maximum shifts in ideological attitudes and beliefs between the two waves.
• Figure 2: This figure shows the temporal evolution of daily averaged anger scores for $\mathcal{G}_{ext}$ and $\mathcal{G}_{neu}$ over the year. The blue lines divide the timeline into four distinct quartiles. Read \ref{['appendix:topic-model']} in the Appendix for more detail on how the topic summaries for each quartile were computed.
• Figure 3: This figure shows the temporal evolution of daily averaged anger scores for $\mathcal{C}_{high}$ and $\mathcal{C}_{low}$ over the year. The blue lines divide the timeline into four distinct quartiles. Read \ref{['appendix:topic-model']} in the Appendix for more detail on how the topic summaries for each quartile were computed.
• Figure 4: Sensitivity analysis for data inclusion. Plot (a) illustrates the tradeoff between engagement filters and channel sample size, while (b) shows the impact of video count thresholds on participant retention (selected threshold $x=50$ marked in red).
• Figure 5: Issue specific shifts in attitude scores over the year.