Syllabus: Mining Social Media

By the end of this course, students will be able to:

Identify key influencers and discover important connectors

Create a message propagation strategy and simulate it in a model

Analyze networks and visualize them in dynamic graphs

Assessment:

1. Concept reviews: these are comprised of short five question quizzes that cover the most important concepts and ideas in each lesson. They encourage holistic understanding and are multi-faceted question types (i.e. drag and drop, fill-in-the-blanks, matching, etc).
2. Exercises: these are additional videos that cover the coding functions in the instructional video in more depth. They are project-based and include coding templates for students to strengthen their skills outside of the course.

Materials provided:

1. Accompanying PDFs to use as reference materials
2. R code templates from the instructional videos and exercises
3. Data sets used in the instructional videos and exercises

Course Outline

1. Gathering social media data (27 min)

What is social media today?
Accessing the Twitter API
Formatting social media data
Cleaning social media data

2. Building social media networks (36 min)

Visualizing social media networks
Visualizing interactive networks
Visualizing hierarchical networks
Calculating network metrics

3. Analyzing your network (35 min)

Identifying key connectors
Measuring betweenness
Identifying most important nodes
Calculating importance

4. Analyzing network effects (36 min)

Calculating Twitter networks
Cascading network effects
Simulating network effects
Automating network effects

5. Simulating network dispersion (31 min)

Generating network effects data
Simulating network dispersion
Animating network dispersion
Additional tips and resources

Total instructional time:               2 hrs, 35 min