GDELT just released their new Global Visualization dashboard, and it’s pretty cool. It blinks and flashes, glows and pulses, and is really interesting to navigate. Naturally, as a social scientist who studies conflict, I have some thoughts.
1) This is really cool. The user interface is attractive, it’s easy to navigate, and it’s intuitive. I don’t need a raft of instructions on how to use it, and I don’t need to be a programmer or have any background in programming to make use of all its functionality. If the technology and data sectors are going to make inroads into the conflict analysis space, they should take note of how GDELT did this, since most conflict specialists don’t have programming backgrounds and will ignore tools that are too programming intensive. Basically, if it takes more than about 10 minutes for me to get a tool or data program functioning, I’m probably not going to use it since I have other analytic techniques at my disposal that can achieve the same outcome that I’ve already mastered.
2) Beware the desire to forecast! As I dug through the data a bit, I realized something important. This is not a database of information that will be particularly useful for forecasting or predictive analysis. Well, replicable predictive analysis at least. You might be able to identify some trends, but since the data itself is news reports there’s going to be a lot of variation across tone, lag between event and publication, and a whole host of other things that will make quasi-experiments difficult. The example I gave to a friend who I was discussing this with was the challenge of predicting election results using Twitter; it worked when political scientists tried to predict the distribution of seats in the German Bundestag by party, but then when they replicated the experiment in the 2010 U.S. midterm elections it didn’t work at all. Most of this stemmed from the socio-linguistics of political commentary in the two countries. Germans aren’t particularly snarky or sarcastic in their political tweeting (apparently), while Americans are. This caused a major problem for the algorithm that was tracking key words and phrases during the American campaign season. Consider, if we have trouble predicting relatively uniform events like elections using language-based data, how much harder will it be to predict something like violence, which is far more complex?
3) Do look for qualitative details in the data! A friend of mine pointed out that the data contained on this map is treasure trove of sentiment, perception and narrative about how the media at a very local level conceptualizes violence. Understanding how media, especially local media, perceive things like risk or frame political issues is incredibly valuable for conflict analysts or peacebuilding professionals. I would argue that this is actually more valuable than forecasting or predictive modeling; if we’re honest with ourselves I think we’d have to admit that ‘predicting’ conflict and then rushing to stop it before it starts has proven to be a pretty lost endeavor. But if we understand at a deeper level why people would turn to violence, and how their context helps distill their perception of risk into something hard enough to fight over, then interventions such as negotiation, mediation and political settlements are going to be better tailored to the specific conflict. This is where the GDELT dashboard really shines as an analytic tool.
I’m excited to see how GDELT continues to make the dashboard better – there are already plans to provide more options for layering and filtering data, which will be helpful. Overall though, I’m excited to see what can be done with some creative qualitative research using this data, particularly for understanding sentiment and perception in the media during conflict.