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ECPR 50th Anniversary Fund

WA110 - Introduction to Discourse Network Analysis (DNA)

Instructor Details

Instructor Photo

Philip Leifeld

University of Glasgow

Instructor Bio

Until March 2019, Philip Leifeld is Professor of Research Methods at the University of Glasgow in the School of Social and Political Sciences. From April 2019, he takes up a post as Professor of Comparative Politics in the Department of Government at the University of Essex.

Philip's research interests are social and political networks, quantitative methods, policy debates, and the study of policy processes.

His work has appeared in a number of journals, such as the American Journal of Political Science and the Journal of Statistical Software.

Twitter  @PhilipLeifeld

Course Dates and Times

Friday 2 March
13:00–15:00 and 15:30–17:00

Saturday 3 March
11:00–12:00 / 13:00–14:30 and 15:00–16:30

Prerequisite Knowledge


Short Outline

You will learn the basics of discourse network analysis, a mixed-methods technique that combines qualitative content analysis with quantitative social network analysis.

This technique can be used to study the development of actors and coalitions in policy debates and other kinds of discussions over time based on text data, such as newspaper articles or Congressional testimony.

I will introduce the software Discourse Network Analyzer (DNA) 2.0 and the R package rDNA.

You will learn

  • how to code statements of actors
  • how to export networks from the coded data
  • how to analyse them in network analysis software such as visone or statnet.

The course introduces the various data transformations and methods available, and discusses ways to get from the initial research idea to a temporal analysis of the discourse network for the purposes of description, exploration, and inference.

Long Course Outline

Discourse network analysis is a toolbox of research methods for the analysis of actor-based debates, such as policy debates or political discussions. Examples include the policy debates on climate change, pension politics, or around the introduction of large infrastructure projects.

Political actors typically include organisations (interest groups, political parties, government agencies etc.) or individual persons (legislators, celebrities etc.). These actors make statements about policy instruments, solution concepts, narratives, frames, issues, arguments etc. in the media or other arenas, and these statements are temporally and cross-sectionally interdependent. Actors build coalitions in a debate by reinforcing each other’s statements or making similar statements, and they frequently contradict each other over time among these coalitions.

The goal of discourse network analysis is to explore, describe, and draw inferences about these processes, based on text data and based on a manual qualitative coding in combination with quantitative social network analysis.

The short course will first introduce a few examples from the literature on discourse networks, define the key concepts, and discuss theoretical frameworks that are compatible with the methodological approach of discourse network analysis.

We will then consider different text sources, different types of debates, and obstacles in the coding process.

I will introduce the software Discourse Network Analyzer (DNA) 2.0 in a hands-on computer lab session, and after demonstrating the coding process, we will proceed to analyse the resulting network data in external network analysis software packages, such as visone.

The next theoretical block will cover the different export options and algorithms available to the user when exporting network data from DNA. Different data transformations are applicable depending on the type of debate, the nature of the data-generating process, and the goals of the analysis.

We will consider, using DNA and network visualisation software like visone, how the choice of the algorithm or method leads to different results, why that is the case, and what method to choose in a given situation.

We will also cover basic network analysis techniques useful for the analysis of discourse networks (such as community detection algorithms and centrality), and we will briefly consider their implementation in software packages.

I will then introduce rDNA, a package for the statistical computing environment R that lets the user import network data from DNA directly into R.

We will discuss several best practices for cluster analysis and other procedures with the data in R, and we will discuss options for the temporal analysis of coalitions and other key features of a debate over time.

Finally, we will briefly introduce the statistical or inferential analysis of temporal discourse network data using relational event models for bipartite signed graphs, as implemented in the R package rem, and we will discuss the data requirements and theoretical insights to be gained from such an inferential analysis.

The course is primarily based on lectures and lab tutorials, but you will get the opportunity to discuss your own projects and work with your own data in the tutorial lab sessions. You should therefore have your text data ready in machine-readable form, and bring your own laptop.

This introductory short course will also cover some advanced topics like statistical analysis in R and network analysis in visone. While existing skills in these domains would be an advantage, the course will introduce these skills as far as possible also to a lay audience. To follow the parts that focus on R, however, basic familiarity with R is required.

Please note: this course is neither an introduction to the general principles of qualitative inquiry or content analysis, nor an introduction to quantitative text analysis or machine learning. Its focus is specifically on the methodological toolbox of discourse network analysis, from the conceptual stage through manual coding up to inferential network analysis of discourse network data.

Day-to-Day Schedule

Day-to-Day Reading List

Software Requirements

If you want to follow the software lab session, please instal a recent Java version (at least Java 8) on your laptop. Download Java

Download the most recent versions of the Discourse Network Analyzer (DNA) 2.0 (all files listed under the most recent release version) and the most recent version of visone (the .jar file, irrespective of operating system).

Instal the statistical computing environment R on your laptop, including the latest versions of

  • rJava
  • rDNA
  • statnet
  • vegan.

MacOS users sometimes experience problems installing rJava. If you do, see this tutorial

All software is free to download and use; some, but not all, is open-source.

Hardware Requirements

Please bring your own laptop (Windows, Linux, or Mac).


Brandt, R. 2017. Die optimale Standortwahl von Stromerzeugungsanlagen: Politikwissenschaftliche Analyse von Steuerungsinstrumenten. Baden-Baden: Nomos.

Butts, C. T. 2008. A Relational Event Framework for Social Action. Sociological Methodology, 38(1):155–200.

Butts, C. T. 2008. network: A Package for Managing Relational Data in R. Journal of Statistical Software, 24(2):1–36.

Butts, C T. 2008. Social Network Analysis with sna. Journal of Statistical Software 24(6):1-51.

Fisher, D. R., J. Waggle and P. Leifeld. 2013. Where does Political Polarization Come From? Locating Polarization Within the U.S. Climate Change Debate. American Behavioral Scientist 116(3):523-545.

Goodreau, Steven M., Mark S. Handcock, David R. Hunter, Carter T. Butts and Martina Morris. 2008. A statnet tutorial. Journal of Statistical Software 24(9): 1-26.

Handcock, M. S., Hunter, D. R., Butts, C. T., Goodreau, S. M., and Morris, M. (2008). statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data. Journal of Statistical Software 24(1):1–11.

Nagel, M. 2015. Polarisierung im politischen Diskurs. Eine Netzwerkanalyse zum Konflikt um “Stuttgart 21”. Berlin: Springer.

The following other ECPR Methods School courses could be useful in combination with this one in a ‘training track .
Recommended Courses Before

Winter School

Introduction to Applied Social Network Analysis

Introduction to R

Recommended Courses After

Summer School

Inferential Network Analysis

Additional Information


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 in due time.

Note from the Academic Convenors

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, contact the instructor before registering.

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