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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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