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Level
Level: IntermediateSoftware
Member rate £572.50
Non-Member rate £1,145.00
Early bird discount available until 14 December 2026.
Save £45.00 Loyalty discount applied automatically*
Save 5% on each additional course booked
* If you attended a qualifying previous Methods School in 2026, you qualify for £45.00 off your course fee.
Date: Monday 22 – Friday 26 February 2027
Time: 13:30 – 16:30 CET
This interactive course uses advanced pedagogical tools and is designed for master's-level students and above. It runs as three-hour live sessions on Zoom over five days, with course materials and pre-course readings available on our Learning Management System at least two weeks before the start. Office hours are also available for one-to-one consultations on your own project.
The course is limited to 18 participants, ensuring that the teaching team can address the individual needs of each participant.
The abundance of textual data provides a rich source of information on social and political behaviour, and social scientists increasingly use computational methods to extract insights from it. At the same time, AI systems built on these same methods now shape how citizens find political information and how political actors communicate. This course equips you with the foundational knowledge and skills to manage and analyse textual data using R, and shows how those skills can be turned towards studying AI itself as a political phenomenon.
By the end of the course, you will:
3 ECTS credits awarded for engaging fully in class activities.
1 additional ECTS credit awarded for completing a post-course assignment.
Zoe Greene is a Reader at the University of Strathclyde. Her research interests include quantitative text analysis and machine learning approaches to studying political parties, parliaments and elections.
Her work has been published in journals including, the British Journal of Political Science, European Journal of Political Research, Journal of European Public Policy, Party Politics and Electoral Studies. You can find more information on Dr Greene’s research, teaching and ongoing projects at zoegreene.com.
This course is structured around lectures and labs, with each session comprising a 1.5-hour lecture followed by a 1.5-hour lab. The lectures will provide an overview of relevant concepts and statistical foundations necessary to apply machine learning approaches to textual data, as well as highlight recent applications of these methods in the social sciences. The lab sessions will allow students to gain hands-on experience by applying these methods to political and social science data using common R packages, including the quanteda suite.
Every day combines lecture and applied lab settings, and will integrate approaches for evaluating NLP and LLM type outputs for evaluating AI models.
This session will introduce you to the theoretical assumptions and key concepts for performing quantitative text analysis, and will showcase several features of the Quanteda package in R. Through examples, the session will illustrate how researchers can use both quantitative and qualitative analysis to gain insights from textual data.
Keyword searches and dictionary-based approaches offer a straightforward method for analysing most texts. One practical application is the study of sentiment, as text often contains clues to the writer's tone, emotion, or attitude. In this session, we will provide an overview of commonly used techniques for extracting this information in political contexts.
Scholars interested in identifying differences between texts based on underlying concepts such as ideology or institutional origin often turn to scaling and topic models. This session will introduce you to Wordfish and Wordscores, two common scaling models, and provide an overview of approaches for validating their estimates. The session will also cover topic models, which can reveal a set of topics within documents with little prior knowledge of their contents. Specifically, the classical LDA model and the Structural Topic Model will be discussed. Finally, the session will explore various applications of these tools.
Supervised approaches to text classification involve using existing annotated text to predict the content of uncoded texts. These methods have enabled major advancements in artificial intelligence, and are widely used by organisations like Google and Facebook for analysing massive corpora of text. In addition, these approaches are highly relevant for measuring political concepts. This session will cover the logic of training and evaluating classification models, followed by exploration of specific applications in political science.
Classification based approaches can be used to predict values of interest such as the issues and positions, sentiment or emotions, and other dimensions of text that researchers have traditionally used content analysis to derive.
The final day will involve broader discussion and group work to assess AI model outputs.
The post-course assignment is optional and will be an analysis of your own data applied to class topics. Completing the post-course assignment will earn you one extra ECTS credit.
A basic understanding of R is highly recommended.
Please have R and RStudio installed before the course.
Allow 3–4 hours per week for background reading.
The course includes three hours of live teaching each day for five days, alongside readings and other learning activities.
Once you have registered and paid, you will receive access to our Learning Management System (LMS) approximately two weeks before the course start date. Course materials, including pre-course readings, will be available here. Allow approximately 20 hours per week before the course to review the materials and complete readings and any pre-course tasks.
During the course week, you should allow approximately two to three hours per day for preparation and assignments, in addition to the live teaching sessions.
The course offers the opportunity to earn three ECTS credits. To earn a fourth credit, you will need to complete a post-course assignment involving approximately 25 hours of work.
This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.
By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach prerequisite items. If in doubt, please contact us before registering.