Social graphs and interactions

• Explore web APIs for data collection.
• Apply a high level programming language (e.g. Python) to utilize such APIs for data acquisition.
• Apply natural language processing to represent statistical structures in text and analyze the content.
• Apply and discuss the main strategies for detecting sentiment in media (e.g. text, music, images, etc).
• Apply standard algorithms to recommend media (text, audio, video) according to user preferences and user context (friends, mood, location, etc).
• Assess basic metrics for complex networks, and model social relations based on network analysis.
• Implement software for detecting communities in social networks and analyze the communities using network metrics
• Quantify relations in social networks to analyze their dynamics, using measures from complex network theory.

The course is based on analyzing data from online social networks (e.g. Twitter, Wikipedia, and Facebook), as well as working with quantitative text analysis. The course is structured around short lectures combined with exercises, as well as a high degree of independent project work.

02100/02101, The course involves work with high level programming languages (e.g. Python), so practical programming experience is recommended (e.g. in Python/Java/JavaScript/C/C++)

Lectures and group work with projects.

It is recommended to bring your own laptop computer to carry out exercises in the course.