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This course is designed for graduate students, particularly PhD students, to provide a comprehensive introduction to social computing research. The course covers how social computing systems shape human interaction, information exchange, collaboration, and collective behavior in online environments. Core topics include social influence and network effects, collaborative and peer production systems, algorithmic curation, content amplification, and online social dynamics. Additional topics include governance and content moderation, online information integrity, auditing of socio-technical systems, antisocial behavior online, and social AI systems. Students will read research papers and engage in writing, group discussion, and oral presentations. The goal of the course is to provide students with the foundations for understanding and extending the current state of the art in social computing research. Required Course Background: At least one graduate-level computer science course in a related area such as HCI, data science, machine learning. Students must be comfortable with reading recent research papers, critically analyzing empirical methods and discussing key concepts and ideas.