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Mining Social Media + Network Diffusion

Sample video: What is social media today?

Mining Social Media + Network Diffusion

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Create interactive visualizations with clickable charts, graphs, maps and networks

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Publish dynamic graphs and applications to your blog or website

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Collect and manipulate data from almost any website, saving you time and creating new possibilities

PrerequisitesIntro to Network Analysis
Instruction2 hours, 40 min
Practice10 to 15 hours

Syllabus: Mining Social Media

This course is designed for students who have taken Data Society’s Introduction to Network Analysis course or who have some experience in network analysis. This 2 ½ hour course teaches students how to pull and clean social media data, identify the most important nodes in a network, and build a dispersion simulation within a network.

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

Merav Yuravlivker

Merav Yuravlivker is a nationally ranked instructor and co-founder of Data Society. She used data-driven strategies in the classroom to maximize educational outcomes and has over 10 years of experience in instructional design, training, and teaching. Merav has helped bring new insights to businesses and move their organizations forward through implementing data analytics strategies.

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