Mining Texts to Generate Fuzzy Measures of Political Regime Type at Low Cost.  Reposted from Dart Throwing Chimp, by Jay Ulfelder.

Political scientists use the term “regime type” to refer to the formal and informal structure of a country’s government. Of course, “government” entails a lot of things, so discussions of regime type focus more specifically on how rulers are selected and how their authority is organized and exercised. The chief distinction in contemporary work on regime type is between democracies and non-democracies, but there’s some really good work on variations of non-democracy as well (see here and here, for example).

Unfortunately, measuring regime type is hard, and conventional measures of regime type suffer from one or two crucial drawbacks.

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This post was written by Jay Ulfelder and originally appeared on Dart-Throwing Chimp. The work it describes is part of the NSF-funded MADCOW project to automate the coding of common political science datasets.

Guess what? Text mining isn’t push-button, data-making magic, either. As Phil Schrodt likes to say, there is no Data Fairy.

I’m quickly learning this point from my first real foray into text mining. Under a grant from the National Science Foundation, I’m working with Phil Schrodt and Mike Ward to use these techniques to develop new measures of several things, including national political regime type.

I wish I could say that I’m doing the programming for this task, but I’m not there yet. For the regime-data project, the heavy lifting is being done by Shahryar Minhas, a sharp and able Ph.D. student in political science at Duke University, where Mike leads the WardLab. Shahryar and I are scheduled to present preliminary results from this project at the upcoming Annual Meeting of the American Political Science Association in Washington, DC (see here for details).

When we started work on the project, I imagined a relatively simple and mostly automatic process running from location and ingestion of the relevant texts to data extraction, model training, and, finally, data production. Now that we’re actually doing it, though, I’m finding that, as always, the devil is in the details. Here are just a few of the difficulties and decision points we’ve had to confront so far.

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Improvised explosive devices, or IEDs, were extensively used during the US wars in Iraq and Afghanistan, causing half of all US and coalition casualties despite increasingly sophisticated countermeasures. Although both of these wars have come to a close, it is unlikely that the threat of IEDs will disappear. If anything, their success implies that US and European forces are more likely to face them in similar future conflicts. As a result there is value in understanding the process by which they are employed, and being able to predict where and when they will be used. This is a goal we have been working on for some time now as part of a project funded by the Office of Naval Research, using SIGACT event data on IEDs and other forms of violence in Afghanistan.


Explosive hazards, which include IEDs, for our SIGACT data.

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Thailand’s Army chief General Prayuth announces the coup on television on 22 May 2014. Source: SCMP

This morning (May 22nd, 2014, East Coast time), the Thai military staged a coup against the caretaker government that had been in power for the past several weeks, after months of protests and political turmoil directed at the government of Yingluck Shinawatra, who herself had been ordered to resign on 7 May by the judiciary. This follows a military coup in 2006, and more than a dozen successful or attempted coups before then.

We predicted this event last month, in a report commissioned by the CIA-funded Political Instability Task Force (which we can’t quite share yet). In the report, we forecast irregular regime changes, which include coups but also successful protest campaigns and armed rebellions, for 168 countries around the world for the 6-month period from April to September 2014. Thailand was number 4 on our list, shown below alongside our top 20 forecasts. It was number 10 on Jay Ulfelder’s 2014 coup forecasts. So much for our inability to forecast (very rare) political events, and the irrelevance of what we do.

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Recently, Syrian rebels (under EU embargo until mid-2013) have relied on weapons smuggled from neighboring states including Iraq, Lebanon, and Turkey (source).

Recently, Syrian rebels (under EU embargo until mid-2013) have relied on weapons smuggled from neighboring states including Iraq, Lebanon, and Turkey (source).  Image from

Why do arms embargoes fail? Despite their frequent use by international organizations like the United Nations and the European Union, arms embargoes suffer from a poor record of success. For half a century now, multilateral arms embargoes have been the primary tool used to fight the proliferation of small arms and light weapons (SALW) to conflict zones and perpetrators of mass violence. These agreements between countries prohibit the sale of weapons to a particular target country (or sometimes a target organization). However, official reviews and academic studies alike tend to conclude that small arms are still making their way to embargoed actors.

Black markets are often cited as a source of this failure. Still, no large-n studies have presented evidence of increased black market activity in the presence of embargoes. To remedy this, I look for evidence of black market activity in records of legal arms trades. The data reveal that arms embargoes are associated with a substantial increase in the value of arms imports into nearby states. Given previous research on the nature of black market arms trade, this seems likely to result from an incentive for neighboring states to import more weapons that will then be transferred illegally to the embargoed state.

Black market arms transfers are difficult to study. Most of what we know about illicit arms transfers comes from those cases where somebody has made a mistake and the illicit activity has been uncovered. Apart from those few select cases, reliable data on actual illegal arms transfers is unavailable. Nonetheless, the illicit arms trade is big business, measuring roughly one billion USD per year.

Embargoed states and their neighbors.

Embargoed states and their neighbors. Embargoes based on data from Erickson (2013), Journal of Peace Research.

Black markets are of particular concern in situations where the legal supply of weapons is low but the demand is high. These circumstances often apply to criminal organizations, rebel groups, and embargoed states. While these illicit trades are difficult to collect data on systematically, most of the weapons involved begin as legally-traded arms. They are traded legally and then diverted from their authorized recipients. Arms embargoes provide an interesting case for the study of illicit arms. Those countries that border embargoed states can take advantage of their shared border to traffic illegal arms to the embargoed neighbor without fear of discovery by a third party. Therefore, if embargoed countries circumvent those embargoes by purchasing arms illicitly, we should expect to see an increase in the arms imported to their neighbors.

I have used data on multilateral arms embargos and legal arms transfers to test this proposition. Statistical models reveal that arms embargoes are indeed associated with greater levels of weapons imports in nearby countries. In fact, the predicted increase is substantial: those countries that border embargoed countries are estimated to import 38% more arms than they would have had they not been neighboring an embargoed country (measured in value, constant 2000 USD). This can translate into hundreds of thousands or even millions of dollars worth of additional weapons. Furthermore, this result takes into account both domestic and international conflict as well as other predictors of arms imports like the overall level of arms imports to the region, government type, and GDP per capita. On the other hand, this analysis indicates that arms embargoes are indeed effective at stemming the flow of legal arms into embargoed countries. Countries targeted by an embargo are predicted to import, on average, 63% fewer arms than they would otherwise.

Predicted levels of arms imports for a hypothetical median state bordering an embargoed state and not bordering an embargoed state.  Fixed effect uncertainty included.  Based on 100,000 simulations.

Predicted levels of arms imports for a hypothetical median state bordering an embargoed state and not bordering an embargoed state. Fixed effect uncertainty included. Based on 100,000 simulations.

Arms embargoes appear to effectively decrease the legal, or recorded, sale of arms to target states. However, this effect is accompanied by a significant increase in the level of arms imported to the surrounding region. Absent other possible explanations, it seems likely that many of these arms are destined for the embargoed country. Effective arms control measures must account for the regional conditions that may undermine nonproliferation efforts.

Large-scale event data based on worldwide media reports already help us to explain and forecast crises events such as civil wars or insurgencies. But the millions of data points provided by ICEWS or GDELT are a treasure trove for social scientists interested in all kinds of topics, whether they involve violence or not.

For example, they can be used to look at the way politicians interact with each other. A lot of research on political competition in the past two or three decades has focused on party positions and politicians’ ideological leanings, fueled by the convenient availability of suitable data (i.e. NOMINATE and the Comparative Manifestos Project). But political competition is about more than just ideology and policy positions. Recent contributions on the Monkey Cage (here and here) have pointed out that the discussion about polarization in the US is to a significant degree about the way politicians interact with each other: that they are more interested in attacking each other verbally, rather than “getting things done” for the good of the country. Arguably, this kind of behavior is responsible for at least part of the gridlock and lack of legislative productivity in Washington even in areas where there is significant bipartisan consensus about policy. However, serious empirical investigations into the way politicians interact with each other have been largely absent, the main reason being a lack of suitable data. But the availability of large-scale media event data can help to change that.

The machine-coded media stories that make up the ICEWS (or GDELT) data provide fine-grained information about how politicians publicly interact with each other, and with other societal actors. They record when one politician criticizes or denounces someone, and they also document when two actors praise each other or express a desire to work together. This allows us to analyze conflict and cooperation between political actors in a systematic manner. In a new working paper, I use the ICEWS event data to analyze the way parties interacted in the 11 Eurozone countries between 2001 and 2011.

I divide the events into two categories, cooperative (e.g. one actor praises another) and conflictual (e.g. one actor criticized another), based on the CAMEO codebook. For each country, the data provide between 2000 and 30,000 events, involving between 125 and almost 450 actors (parties, NGOs, military, etc.). The actors have a complex network of interactions with each other. To summarize them in a simple and intuitive manner, I estimate latent network models for each country-year. Without getting into the technical details, these models estimate the position of each actor in a hypothetical latent space. Actors that are positioned close together in the latent space have a higher probability of interacting with each other frequently in a cooperative way, while actors positioned far away from each other are likely to interact in a conflictual manner.

Posterior latent space estimates for Greece in 2002, 2006, and 2010. Parties: PASOK (green), ND (blue), KKE (red). All other actors in gray.

Latent space estimates for Greece in 2002, 2006, and 2010. Parties: PASOK (green), ND (blue), KKE (red). All other actors in gray.

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It is the end of the year, and we’re supposed to be reflective.  But not too much. After all, this is a blog. The colleagues in this lab are terrific and it serves to pause for a moment to reflect on one tiny aspect of their accomplishments this last year: their publications.  I do think publishing is broken, but not everyone is ready or able to abandon ship just yet.  You will read no whine about publishing here. Well, at least not today.  In any case, we have been remarkably successful as you can see below. Why?

One reason is that research in 2013 is a collaborative process. It took sixteen of us to produce the dozen or so articles listed below. This means that we can do a lot collectively, but each of us has to do a lot individually to make that happen.  Indeed, we can do more collectively than each of us can do individually. Partially, this is supported by good will and common purpose, but more than a sliver of dropbox, github, and skype are involved as well. And some tolerance for the 24/7 lifestyle that everyone leads.  We live in a fantastic world where anyone with a laptop and internet access can really collaborate with colleagues who might be (as “we” have been at various times) in London, India, Seattle, Pennsylvania, Korea, Mexico, Austin, Croatia, Madison, New York, Santiago, Berlin, or Boulder Colorado.

It is also important to recognize that we have made a decision to join together and work together on projects. Most of these projects have a common theme, sure. But that theme is fairly permeable and open. And, the amount of what we really do not know about political life remains enormous. As a result, opportunities abound. But “suddenly” we have a lot of new ways of thinking about and investigating the perplexing world we live in.  We are not really always stuck in the corner solving things the so-called Gell-man way (sitting in our office and thinking real hard).  That may be helpful, but so is doing proofs, writing simulation code, querying databases, and writing computer programs. These things are especially helpful after a bit of reflection, but it turns out that they work better if the ideas being investigated have been annealed by discussion and dialogue among interested colleagues, who often see weakness and nuance where if left to our own devices  we might not perceive even the most glaring imperfection, let along the smallest.

Collaboration with bright colleagues is terrifically fun, and I am truly grateful to have the opportunity to participate with them in this lab.  Here is a list of projects that we published in the year 2013, minus a few things still snagged by reviewer number three.. Stay tuned for more good things in 2014 and for a forthcoming post on current lab projects.

  1. Michael D. Ward, Nils W. Metternich, Cassy L. Dorff, Max Gallop, Florian M. Hollenbach, Anna Schultz, and Simon Weschle. “Learning from the Past and Stepping into the Future: Toward a New Generation of Conflict Prediction,” International Studies Review (2013) 15, 473–490.
  2. Michael D. Ward, Cassy L. Dorff. “Les réseaux, les dyades et le modèle des relations sociales.” Liber amicorum: Hommage en l’honneur du Professeur Jacques Fontanel. Ed. Liliane Perrin-Bensahel and Jean-Francois Guilhaudis L’Harmattan, March, 2013: 271-288.
  3. Kristin M. Bakke, John V. O’Loughlin, Gerard O’Tuathail, and Michael D. Ward. “Convincing State-Builders? Disaggregating Internal Legitimacy in Abkhazia.”International Studies Quarterly 58.3 (2013).
  4. Cassy L. Dorff and Michael D. Ward. “Networks, Dyads, and the Social Relations Model.” Political Science Research Methods 1.2 (December, 2013): 159-178.
  5. Nils W. Metternich Cassy L. Dorff, Max Gallop, Simon Weschle & Michael D. Ward. “Anti-Government Networks in Civil Conflicts; How Network Structures Affect Conflictual Behavior.” American Journal of Political Science 57.4 (October, 2013): 777-1028.
  6. Michael D. Ward, John S. Ahlquist, and Arturas Rozenas. “Gravity’s Rainbow: A Dynamic Latent Space Model for the World Trade Network.” Network Science 1.1 (March, 2013): 95-118.
  7. Xun Cao and Michael D. Ward. “Do Democracies Attract Portfolio Investment? Transnational Portfolio Investments Modeled as Dynamic Network.” International Interactions 39.1 (2013 in press): in press.
  8. Jacob M. Montgomery, Florian M. Hollenbach, and Michael D. Ward. “Aggregation and Ensembles: Principled Combinations of Data.” PS: Political Science & Politics 46.1 (January, 2013): 43-44.
  9. Kristian Skrede Gleditsch and Michael D. Ward. “Forecasting is Difficult, Especially about the Future: Using Contentious Issues to Forecast Interstate Disputes.”Journal of Peace Research 50.1 (2013): 17-31.
  10. Jan Pierskalla and Florian M. Hollenbach. “Technology and Collective Action: The Effect of Cell Phone Coverage on Political Violence in Africa.” American Political Science Review 107.2 (2013): 207-224.
  11. Matthew Dickenson. “Leadership Transition and Violence in Mexican Drug Trafficking Organizations 2006-2010.”  Journal of Quantitative Criminology (2013): tba.
  12. Simon Weschle. “Two Types of Economic Voting: How Economic Conditions Jointly Affect Vote Choice and Turnout.” Electoral Studies in press (2013).
  13. December 30 update: Jacob M. Montgomery and  Josh Cutler. “Computerized Adaptive Testing for Public Opinion Surveys.” Political Analysis 21.2 (2013): 172-192.