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Explore a research seminar that challenges conventional assumptions about algorithmic radicalization on social media platforms through rigorous empirical analysis. Examine how UCLA researcher Homa Hosseinmardi addresses the methodological gap in evaluating claims about recommendation algorithms amplifying problematic content by introducing innovative "counterfactual bots" methodology. Learn about comprehensive research findings from YouTube that reveal distinct communities of news consumers ranging from moderate to extreme viewpoints, while discovering evidence that contradicts popular narratives about algorithmic radicalization. Understand how the study demonstrates that relying exclusively on recommendation systems actually results in less partisan content consumption on average, with the most pronounced effects observed among heavy partisan consumers. Gain insights into the complex interplay between user preferences, platform features, and broader web supply-and-demand dynamics that shape political news consumption patterns. Discover methodological innovations that enable researchers to disentangle user agency from algorithmic influence by comparing real user consumption patterns with rule-based counterfactual trajectories. Engage with cutting-edge research from the OASIS Lab that takes a holistic, large-scale approach to understanding sociotechnical systems and information ecosystems, focusing on safety and trustworthiness in online environments.
