It’s possible to build powerful filtering systems by combining software and people, incorporating both algorithmic content analysis and human actions such as follow, share, and like. We’ll look recommendation systems, the Facebook news feed, and the socially-driven algorithms behind them. We’ll finish by looking at an example of using human preferences to drive machine learning algorithms: Google Web search.

Topics: Social filtering. The network structure of Twitter. Social software. Comment ranking on Reddit. Confidence sorting. User-item recommendation and collaborative filtering. Hybrid filters. What makes a good filter?

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.