Assess learners' ability to analyze how recommender algorithms shape information exposure and influence public opinion on social media. Coverage includes algorithm types (collaborative filtering, content-based, ranking), personalization and feedback loops, metrics of exposure and engagement, phenomena such as filter bubbles and polarization, common research methods (A/B testing, observational studies, causal inference, network analysis), and ethical/policy implications. Learners should interpret empirical findings, critique study designs and biases, and propose evaluation or mitigation strategies for algorithmic influence.