Introduction to Social Media Analytics

This comprehensive course explores the intersection of social media platforms and network science, providing students with essential skills for analysing digital social interactions. Beginning with graph theory fundamentals, students learn to model social media data as networks and apply mathematical frameworks to extract meaningful insights. The curriculum progresses through advanced network analysis, centrality measures, and community detection algorithms. Students master key concepts, including degree centrality, betweenness analysis, PageRank algorithms, and information diffusion models. Practical applications focus on influencer identification, recommendation systems, viral marketing strategies, and community leader detection. Advanced modules cover machine learning techniques for social media, including language analysis, fake news detection, and behavioural prediction. Students explore ethical considerations in social media research, privacy preservation, and responsible AI applications. The course emphasises hands-on implementation using NetworkX, real-world case studies, and industry-relevant projects. By completion, students will be equipped to analyse social media networks professionally, develop recommendation algorithms, design viral marketing campaigns, and conduct ethical social media research. This course is ideal for data scientists, marketing professionals, researchers, and anyone seeking to understand the mathematical foundations of social media analytics.