Online social media analytics is an emerging field in modern science, thanks to the growing abundance of data.
Seeking to enhance our understanding of the principles and patterns of the information exchange and opinion formation in the society, this course is intended to review key concepts involved in the analysis of online user contributed content.
It will rely on the scholarship in data analysis and mining, with the purpose of taking an in-depth look at theories, methods, and tools to examine the content, structure and dynamics of social media.
The course offers an introduction to the key theoretical concepts in text and social network analytics, and primarily aims at supporting future applied investigations of interest to the audience, through hands-on practice tutorials.
• Introduction to Opinion Mining / Sentiment Analysis
• Topic modeling in R
• Text Analytics on a Twitter stream
• Mining Consumer Sentiment
• Introduction to Social Network Analysis
• Network Data Manipulation in R
• Social Network Analysis Metrics: node-based, local and global
• Network Visualization: static and temporal
• Hypothesis Testing
Analysis of Cascades
• Introduction to Cascade Formation and Social Influence
• Activity Dynamics Analysis for Twitter hashtags
• Predictive Analysis of Retweeting / Reposting Behavior
The students will be expected to work with mathematical models and analytical reasoning. Basic knowledge of matrix algebra, statistical analysis, and probability theory is required. Programming experience (in some language) is strongly encouraged. Knowledge of stochastic processes and optimization techniques is encouraged but not required.