Syllabus – Fall 2014

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The course is a hands-on introduction to the areas of computer science that have a direct relevance to journalism, and the broader project of producing an informed and engaged public. We will touch on many different technical and humanistic topics: information recommendation systems but also filter bubbles, principles of statistical analysis but also the social processes which generate data, social network analysis and its role in investigative journalism, visualization techniques and the cognitive effects involved in viewing a visualization. Assignments will require programming in Python, but the emphasis will be on clearly articulating the connection between the algorithmic and the editorial.

Our scope is wide enough to include both relatively traditional journalistic work, such as computer-assisted investigative reporting, and the broader information systems that we all use every day to inform ourselves, such as social media. The course will provide students with a thorough understanding of how particular fields of computational research relate to journalism practice, and provoke ideas for their own research and projects.

Research-level computer science material will be discussed in class, but the emphasis will be on understanding the capabilities and limitations of this technology. Students with a CS background will have opportunity for algorithmic exploration and innovation, however the primary goal of the course is thoughtful, application-oriented research and design.

Assignments will be completed in groups (except dual degree students, who will work individually) and involve experimentation with fundamental computational techniques. Some assignments will require intermediate level coding in Python, but the emphasis will be on thoughtful and critical analysis. As this is a journalism course, you will be expected to write clearly.

Format of the class, grading and assignments.
This is a fourteen week course for Masters’ students which has both a six point and a three point version. The six point version is designed for CS & journalism dual degree students, while the three point version is designed for those cross listing from other schools. The class is conducted in a seminar format. Assigned readings and computational techniques will form the basis of class discussion. Throughout the semester we will invite guest speakers with expertise in the relevant areas to talk about their related journalism, research, and product development The course will be a graded as follows:

  • Assignments: 80%. There will be a homework assignment after most classes.
  • Class participation: 20%

Dual degree students will also have a final project. This will be either a research paper, a computationally-driven story, or a software project. The class is conducted on pass/fail basis for journalism students, in line with the journalism school’s grading system. Students from other departments will receive a letter grade.

Week 1: Basics – 9/5
Slides.
First we ask: where do computer science and journalism intersect? CS techniques can help journalism in four different areas: data-driven reporting, story presentation, information filtering, and effect tracking. Then we jump right in with the concept of data. Specifically, we study the quantification process, leading to feature vectors which are a fundamental data representation for many techniques.

Required

Recommended

  • Precision Journalism, Ch.1, Journalism and the Scientific Tradition, Philip Meyer

Viewed in class

Week 2: Clustering – 9/12
Slides.
A vector of numbers is a fundamental data representation which forms the basis of very many algorithms in data mining, language processing, machine learning, and visualization. This week we will explore two things: representing objects as vectors, and clustering them, which might be the most basic thing you can do with this sort of data. This requires a distance metric and a clustering algorithm — both of which involve editorial choices! In journalism we can use clusters to find groups of similar documents, analyze how politicians vote together, or automatically detect groups of crimes.

Required

Recommended

Viewed in class

Week 3: Text Analysis – 9/19
Slides.
Can we use machines to help us understand text? In this class we will cover basic text analysis techniques, from word counting to topic modeling. The algorithms we will discuss this week are used in just about everything: search engines, document set visualization, figuring out when two different articles are about the same story, finding trending topics. The vector space document model is fundamental to algorithmic handling of news content, and we will need it to understand how just about every filtering and personalization system works.

Required

  • Online Natural Language Processing Course, Stanford University
    • Week 7: Information Retrieval, Term-Document Incidence Matrix
    • Week 7: Ranked Information Retrieval, Introducing Ranked Retrieval
    • Week 7: Ranked Information Retrieval, Term Frequency Weighting
    • Week 7: Ranked Information Retrieval, Inverse Document Frequency Weighting
    • Week 7: Ranked Information Retrieval, TF-IDF weighting

Recommended

Examples

Assignment: TF-IDF analysis of State of the Union speeches.

Week 4: Information overload and algorithmic filtering –  9/26
Slides.
This week we begin our study of filtering with some basic ideas about its role in journalism. Then we shift gears to pure algorithmic approaches to filtering, with a  look at how the Newsblaster system works (similar to Google News.)

Required

Recommended

Week 5: Social filtering – 10/3
Slides.
We have now studied purely algorithmic modes of filtering, and this week we will bring in the social. The distinction we will draw is not so much the complexity of the software involved, but whether the user can understand and predict the filter’s choices. We’ll look at Twitter as a prototypical social filter and see how news spreads on this network, and tools to help journalists find sources. Finally, we’ll introduce the idea of “social software” use the metaphor of “architecture” to think about how software influences behaviour.

Required

Recommended

Week 6: Hybrid filters, recommendation, and conversation – 10/10
Slides.
We have now studied purely algorithmic and mostly social modes of filtering. This week we’re going to study systems that combine software and people. We’ll look at “recommendation” systems and the socially-driven algorithms behind them. Then we’ll turn to online discussions, and hybrid techniques for ensuring a “good conversation” — a social outcome with no single definition. We’ll finish by looking at an example of using human preferences to drive machine learning algorithms: Google Web search.

Required

Recommended

Assignment – Design a filtering algorithm for status updates.

Week 7: Visualization – 10/17
Slides.
An introduction into how visualisation helps people interpret information. Design principles from user experience considerations, graphic design, and the study of the human visual system. The Overview document visualization system used in investigative journalism.

Required

Recommended

Week 8: Structured journalism and knowledge representation -10/24
Slides.
Is journalism in the text/video/audio business, or is it in the knowledge business? This class we’ll look at this question in detail, which gets us deep into the issue of how knowledge is represented in a computer. The traditional relational database model is often inappropriate for journalistic work, so we’re going to concentrate on so-called “linked data” representations. Such representations are widely used and increasingly popular. For example Google recently released the Knowledge Graph. But generating this kind of data from unstructured text is still very tricky, as we’ll see when we look at the Reverb algorithm.

Required

Recommended

Assignment: Text enrichment experiments using OpenCalais entity extraction.

Week 9: Network analysis – 10/31
Slides.
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.

Required

Recommended

Examples:

Assignment: Compare different centrality metrics in Gephi.

No class November 7th

Week 10: Drawing conclusions from data – 11/14
Slides.
You’ve loaded up all the data. You’ve run the algorithms. You’ve completed your analysis. But how do you know that you are right? It’s incredibly easy to fool yourself, but fortunately, there is a long history of fields grappling with the problem of determining truth in the face of uncertainty, from statistics to intelligence analysis.

Required

  • Correlation and causation, Business Insider
  • The Psychology of Intelligence Analysis, chapters 1,2,3 and 8. Richards J. Heuer

Recommended

Assignment: write a story on the status of women in science.

Week 11: Algorithmic Accountability – 11/21
Slides.
Our society is woven together by algorithms. From high frequency trading to predictive policing, they regulate an increasing portion of our lives. But these algorithms are mostly secret, black boxes form our point of view. We’re at they’re mercy, unless we learn how to interrogate and critique algorithms.

Required

Recommended

Week 12: Privacy, Security, and Censorship – 12/5
Slides.
Who is watching our online activities? How do you protect a source in the 21st Century? Who gets to access to all of this mass intelligence, and what does the ability to survey everything all the time mean both practically and ethically for journalism? In this lecture we will talk about who is watching and how, and how to create a security plan using threat modeling.

Required

Recommended

Assignment: Use threat modeling to come up with a security plan for a given scenario.

Week 13: Tracking flow and impact – 12/12
Slides.
How does information flow in the online ecosystem? What happens to a story after it’s published? How do items spread through social networks? We’re just beginning to be able to track ideas as they move through the network, by combining techniques from social network analysis and bioinformatics.

Required

Recommended

 Final projects due 12/19 (dual degree Journalism/CS students only)

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