Network Theory for Social Media Analysis delves into the intricate relationships and structures that govern social media interactions. This course equips participants with the mathematical tools and analytical frameworks necessary to interpret and visualize social networks effectively. By engaging in hands-on projects, learners will apply theoretical concepts to real-world scenarios, enhancing their ability to derive actionable insights from social media data.
Participants will explore various methodologies, including graph theory, network metrics, and visualization techniques, to analyze user behavior and community dynamics. The course culminates in a final project where learners will present their findings, potentially for publication in Cademix Magazine, fostering an environment of collaboration and professional growth. This program not only enhances analytical skills but also prepares participants for roles that require a deep understanding of social media landscapes.
Introduction to Network Theory and its Relevance to Social Media
Fundamentals of Graph Theory: Nodes, Edges, and Types of Graphs
Analyzing Social Media Platforms: Data Sources and Collection Methods
Key Metrics in Network Analysis: Centrality, Density, and Clustering Coefficients
Visualization Tools for Social Networks: Techniques and Software
Community Detection Algorithms: Identifying Subgroups within Networks
Temporal Dynamics in Social Networks: Understanding Change Over Time
Case Studies: Successful Applications of Network Analysis in Marketing
Project Work: Analyzing a Social Media Dataset of Choice
Final Presentation: Results and Insights from the Project