19 August 2016
Online Social Media Analytics
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
Lecturer: Assistant Professor Alexander Nikolaev (University at Buffalo, United States)
Coordinators: Prof. Jari Veijalainen and Dr. Alexander Semenov (University of Jyvaskyla)
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.
The Summer School annually offers courses for advanced master’s students, graduate students, and post-docs in the various fields of science and information technology.
The most important aims of the Summer School are to develop post-graduates scientific readiness and to offer students the possibility to study in a modern, scientific environment and to create connections to the international science community. The Summer School offers an excellent pathway to develop international collaboration in post-graduate research.
Labs (15 % of full grade each):
• Static Data Collection from Twitter and Topic Modeling (R)
• Streaming Twitter Data and Sentiment Analysis (R)
• Social Network Analysis (R and Gephi)
• Hypothesis Testing and Predictive Analysis (R)
Project Paper: 40 % of full grade
EUR 0: Participating the Summer School is free of charge, but student have to cover the costs of own travel, accommodation and meals at Jyväskylä.
The 26th Jyväskylä Summer School is not able to grant any Summer School students financial support.