STV4020H - Quantitative Text Analysis

Schedule, syllabus and examination date

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Course content

What do parties and politicians talk about? How and how often do they speak about particular issues? How do the media cover particular issues, like immigration? How do citizens interact on Twitter or on Facebook? A common aspect of all these questions is that politicians, media, or citizens all produce texts while communicating. With increased digitalizing and storage of these text footprints, growing amounts of text become available for political research.

It is sometimes difficult or even impossible to read large amounts of text in order to identify patterns that may (or may not) match well with our theories. In such cases, computers may assist human reading and coding of what parties said or how political debates unfolded. Computers also enable us to introduce new approaches to collecting texts and to analyze them in a systematic manner.

The topics covered by this course should be of general interest to all students of political science. You will learn to search documents, to summarize them in a meaningful way, and to analyze the obtained information using methods applicable also to other types of data. Assisted by computers, we will do all this in an efficient manner. Most importantly, you will learn how working with texts can help you achieve your own research aims.

Learning outcome

Knowledge

You will learn:

  • the possible uses of text in political analysis
  • methodologies for quantitatively extracting meaning from text relevant for political communication and public policy

Skills

You will be able to:

  • apply and evaluate cutting-edge quantitative techniques for text analysis
  • identify and assess such techniques’ limitations and applicability for specific research questions
  • gather and analyze text in a scalable manner
  • link current results from quantitative text analysis to relevant political science research questions and map societal and political dynamics

Competences

You will be able to:

  • select and adapt pre-existing quantitative text analysis techniques for their own research
  • gather, structure, pre-process, and manipulate large bodies of text
  • apply different models and summarize them in substantive terms

Admission

Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for in Studentweb.

Students enrolled in other Master's Degree Programmes can, on application, be admitted to the course if this is cleared by their own study programme.

If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures.

Apply for guest student status if you are admitted to another Master's programme.

For incoming students

All Master's courses in Political Science must be registered manually by the Department, they will not appear in Studentweb. Contact your international coordinator at UiO.

Prerequisites

Formal prerequisite knowledge

STV4020A - Forskningsmetode og statistikk or comparable, i.e. understanding of quantitative methods/statistics.

Students are expected to have working knowledge of R, which should cover at least: loading data and packages, data recoding, fitting at least simple regression models (lm() for example), and extracting quantities of interest from these model objects.

Teaching

The course runs for five weeks with two meetings held each week.

These meetings will be a mixture of a) instructor lead introduction of the problem, b) review of the readings/approaches, and c) software demonstration. Accordingly, in-class activity presupposes that participants read the required readings and follow along the implementation examples and exercises.

The meetings will be based on substantive political science problems that have been analyzed using text data (party manifestos, government bills, speeches, social media updates, for example), with supporting readings on the methods employed, and software demonstration and exercises.

Examination

Portfolio examination.

The portfolio consists of:

  • four home assignments during the course (excluding the first week of the course).
  • These home assignments contain both discussion of a particular problem and software application.
  • The home assignments will be between 2000-3000 words, with R code supplied separately to reproduce the results reported and discussed in the assignments.
  • Short feedback is offered on each of the home assignments, with additional detailed feedback available through office hours or request.

The final letter grade will reflect the overall performance throughout the course. You must submit all the assignments in the portfolio in the same semester.

Use of sources and citation

You should familiarize yourself with the rules that apply to the use of sources and citations. If you violate the rules, you may be suspected of cheating/attempted cheating.

Grading scale

Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.

Explanations and appeals

It is recommended to request an explanation of your grade before you decide to appeal.

Appeal

Explanation

The deadline to request an explanation is one week after the grade is published. For oral and practical examinations, the deadline is immediately after you have received your grade.

The explanation should normally be given within two weeks after you have asked for it. The examiner decides whether the explanation is to be given in writing or verbally.

Resit an examination

If you are sick or have another valid reason for not attending the regular exam, we offer a postponed exam later in the same semester.

See also our information about resitting an exam.

Special examination arrangements

Application form, deadline and requirements for special examination arrangements.

Evaluation

The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.

Facts about this course

Credits

10

Level

Master

Teaching

Every autumn

Examination

Every autumn

Teaching language

English