New tool for discourse network analysis

EJPR has just published an article introducing a new tool for ‘discourse network analysis’. Using the tool, you can measure and visualize political discourses and the networks of actors affiliated to each discourse. One can study the actor congruence networks (based on the number of statements actors share), concept congruence networks (based on whether statements are used by an actor in the same way) and trace the evolution of both over time.

Here is a graph taken from the paper which illustrates the actor congruence networks for the issue of software patents in the EU (click to enlarge):

The discourse networks analysis tool is free and available from the website of Philip Leifeld, one of the co-authors of the article. I can’t wait to get my hands on the program and try it out for myself. The tool promises to be an interesting alternative to evolutionary factor analysis – another new method for studying policy frames and discourses that I recently discussed – with the added benefit of being able to present actors and frames in an integrated analysis.  

Here is the abstract of the EJPR article (there are more resources at this website):

In 2005, the European Parliament rejected the directive ‘on the patentability of computer-implemented inventions’, which had been drafted and supported by the European Commission, the Council and well-organised industrial interests, with an overwhelming majority. In this unusual case, a coalition of opponents of software patents prevailed over a strong industry-led coalition. In this article, an explanation is developed based on political discourse showing that two stable and distinct discourse coalitions can be identified and measured over time. The apparently weak coalition of software patent opponents shows typical properties of a hegemonic discourse coalition. It presents itself as being more coherent, employs a better-integrated set of frames and dominates key economic arguments, while the proponents of software patents are not as well-organised. This configuration of the discourse gave leeway for an alternative course of political action by the European Parliament. The notion of discourse coalitions and related structural features of the discourse are operationalised by drawing on social network analysis. More specifically, discourse network analysis is introduced as a new methodology for the study of policy debates. The approach is capable of measuring empirical discourses both statically and in a longitudinal way, and is compatible with the policy network approach.

The decline of the death penalty

I just finished reading ‘The Decline of the Death Penalty and the Discovery of Innocence’ (link, link to book’s website) by Frank Baumgartner, Suzana De Boef and Amber Boydstun. It is a fine study of the rise of the ‘innocence’ frame and the decline of the use of capital punishment in the US (I have recently posted about the death penalty). The book has received well-deserved praise from several academic corners (list of reviews here). In this post I want to focus on several issues that, in my opinion, deserve further discussion.

One of the major contributions of the book is methodological. The systematic study of policy frames (‘discourse’ is a related concept that seems to be getting out of fashion) is in many ways the holy grail of policy analysis – while we all intuitively feel that words and arguments and ideas matter more than standard models of collective decision making allow, it is quite tricky to demonstrate when and how these words and arguments and ideas matter. Policy frame analysis became something of a hype during the late 1970s and the 1980s, but it delivered less than it promised, so people started to look away (as this Google Ngram shows). Baumgartner, De Boef and Boydstun have produced a book with the potential to re-invigorate research into the impact of policy frames.

So far, the usual way to analyze quantitatively policy frames has been to count the number of newspaper articles on a topic, measure their tone (pro/anti) and classify the arguments into some predefined clusters (the frames). This is what the authors do with respect to the death penalty in Chapter 4. They collected all articles on capital punishment listed in the New York Times Index from 1960 till 2005, coded each article for its pro- or anti- death penalty orientation and classified the arguments found in each article into a pre-defined set of 65 possible arguments, clustered along seven dimensions (efficacy, morality, cost, constitutionality, fairness, mode of execution, and international issues) (p.107).  The approach allows one to track total attention to capital punishment, the net tone, and the relative attention to each of the seven dimensions over time. This is useful to identify, for example, the surge in attention after 1995 to issues of innocence and evidence in stories on the death penalty (p.120) which have ‘come to dominate’ the debate. Existing studies of policy frames usually stop here. But as the authors argue:

[The frequency of attention] matters, of course, but also important is the extent to which these arguments are used in conjunction with one another to form a larger cohesive frame. (p.136)

Enter evolutionary factor analysis (Chapter 5). The technique is essentially a series of factor analyses performed on overlapping 5-year time windows. Factor analysis identifies inductively (from the data) which arguments tend to go together. So you start with a factor analysis of the arguments contained in the articles published in 1960 to 1965 treating each year as a single observation. You repeat for each 5-year period (1961 to 1966, 1962-1967, etc.), track the clusters of arguments (the frames) that seem stable, and trace how they move and change over time. Using this approach, the book claims that a set of 16 arguments centered around ‘innocence’ (the frame) emerged in 1992, captured the debate and is still going strong. Since 13 of these arguments are anti-death penalty, the rise of the innocence frame is responsible for the increasingly anti-death penalty tone of the newspaper coverage. As I said, the approach is path-breaking and holds lots of promise, but I have one concern. Currently, each factor analysis is based on 65 variables (since the authors ignore all arguments that appeared less than five times in any five-year period, the effective number of variables is much smaller but still usually greater than the number of observations) and 5 observations only (the years). This introduces lots of noise in the data (as the authors themselves acknowledge) and necessitates a series of more or less arbitrary decision to get rid of statistical flukes. Rules of thumb about sample size in factor analysis often recommend a minimum of 100 observations and at least twice as many observations as variables [factanal in R even refuses to perform the factor analysis with more variables than observations; SPSS obeys]. So there is a potential problem, but what is a bit puzzling to me is that there seems to be a pretty obvious way to address the problem; a way which the authors do not discuss:
Why not run the factor analyses on all articles that appear in a year, taking the individual article as the unit of observation?
True, many articles are coded to feature only one argument, but the median number of arguments per article is two, and there are 1635 articles (so more than 40% of the sample) that have more than two arguments (that’s based on my quick-and-dirty calculations from the replication dataset available here). Apart from providing more observations, taking the article as a unit of observation makes theoretical sense as well – we want to know whether frames dominate individual contributions (articles), as well as the macro-debate in a given year.

Having demonstrated the rise and the recent dominance of the ‘innocence’ frame, in Chapter 6 Baumgartner, De Boef and Boydstun proceed to estimate the impact of ‘net tone’ on public opinion. As explained above, the book attributes the major changes in ‘net tone’ (pro- vs. anti- sentiment of the newspaper articles) to the changing frames, so indirectly this is testing the impact of frames as well. Using a vector error-correction model, the authors argue that levels of public opinion are ‘positively related to levels of homicides [control variable] and pro-death penalty media coverage’ (p.187). Chapter 7 turns to explaining the number of annual death sentences and concludes that both media ‘net tone’ and public opinion are significantly associated with this policy indicator. I wouldn’t be too quick to attribute any causal power to media tone, however. If one takes seriously the first part of the book, then the policy frame emerges as a potential confounding variable that works both directly (through framing the thinking of policy makers, jurors and judges) and indirectly through the media. If that was the case, the effect of media tone would be exaggerated in the statistical models as it would pick up to the direct effect of the policy frame as well. One can make a similar case for the effect of public opinion. I also prefer investigating more directly the direction of causality in such systems of variables that move together over time (using Granger causality tests or VAR) -  I see little theoretical reason why the number of death sentences cannot have an impact on public opinion, for example.

A bigger threat to the integrity of the story about the rise of ‘innocence’ and the decline of the death penalty, however, is the persistence of important differences among states in public opinion and the number of death sentences and executions. Since this book focuses on the tone and framing of the death penalty debate in a national media (NYT), it cannot address the question of cross-state variation. But I think it is a valid question, and one that deserves more research, whether the population and policy makers in some states are less sensitive (immune?) than others to the effects of framing, or whether they are exposed to different media with a different net tone and using a different frame. A recent paper by Kenneth Shirley and Andrew Gelman shows that black males, and to a lesser extent black females, “have shown the sharpest decline in support” over time (p.31, see also Figure 8 ) while the net change in support for the death penalty among non-black men and women is quite small (Figure 9).  It would seem that the ‘innocence’ frame has resonated much more (only?) with black people, and black people have responded stronger, and faster, to the arguments put forward by the frame. Perhaps the fact that many of the individuals exonerated from death row have been black can explain the differentiated impact of the innocence frame. In any case, there are interesting synergies between Shirley and Gelman’s study with its emphasis on individual and state differences and Baumgartner et al.’s focus on variation over time.

To conclude this rather lengthy post, the ‘The Decline of the Death Penalty and the Discovery of Innocence’ uncovers an exciting new direction for policy frames research. In fact, I am already starting a project attempting to apply the evolutionary factor analysis approach to policy framing in the context of anti-smoking policy in Europe.