Network analysis (aka social network analysis, link analysis) is a promising and popular technique for uncovering relationships between diverse individuals and organizations. It is widely used in intelligence and law enforcement, but not so much in journalism. We’ll look at basic techniques and algorithms and try to understand the promise — and the many practical problems.

Topics: What's a social network? Link analysis. Homophily and structural determinants of behavior. Centrality measurements. Community detection and the modularity algorithm. K-core decomposition. SNA in journalism. SNA that could be in journalism.

Instructor: Jonathan Stray
course blog at jmsc.hku.hk/courses/jmsc6041spring2013/

1. Basics of Computational Journalism: Feature Vectors, Clustering, Projections
2. Text Analysis: Tokenization, TF-IDF, Topic Modeling
3. Algorithmic Filters: Information Overload
4. Social and Hybrid Filters: Collaborative Filtering
5. Social Network Analysis: Centrality Algorithms
6. Knowledge Representation: Structured data & Linked open data
7. Drawing Conclusions from Data
8. Security, Surveillance, and Privacy

Computational Journalism is a course given at JMSC during the Spring 2013 semester. It covers, in great detail, some of the most advanced techniques used by journalists to understand digital information, and communicate it to users. We will focus on unstructured text information in large quantities, and also cover related topics such as how to draw conclusions from data without fooling yourself, social network analysis, and online security for journalists. These are the algorithms used by search engines and intelligence agencies and everyone in between.