Many governments put many fund in order to predict civil unrest.
Lately, I have been consulted a lot about how riots in the world can be predicted.
I want to share my personal view of how this can be achieved via a real prediction example I have experienced when using our prediction algorithms that are presented in a paper I co-authored with Eric Horvitz a couple of years ago.
September 22nd, 2013 – It is my morning routine to check out the new predictions made by the system I was coding during my PhD and see how the prediction algorithm can be improved. The main prediction on the screen is written in plain text: “Mass unrest and instability in Sudan”. When clicking on the prediction it reveals the pattern that is going to happen in the future – protests will start, then youth killed by police leading to government instability and mass unrest. The main reason is marked on the screen, quoting a newspaper title from the last few days– “government lifted its gas subsidies.” The historical pattern inferred by the algorithm is written next to it – when governments of countries with rising GDP with high population poverty lift subsidies of common products, student and youth protest start. If youth is killed, mass riots start. When clicking on the pattern, the system reveals the past events the contributed to this hypothesis. The first news title is from January 1st, 2012 describing that Nigeria lifts subsidies on oil, and the next title in the pattern is “Muyideen Mustapha, 23, was reportedly the first person to be killed during the nationwide protests over the lifting of petrol subsidies.” (Twitter by @ocupynigeria), and few days later on January 9:
“Tens of thousands of Nigerians took to the streets in cities across the country on Monday to protest a sudden sharp rise in oil prices after the government abruptly ended fuel subsidies” (NYT) and “Nigeria fuel protests: two killed and dozens wounded as police open fire” (the guardian). ). The last event in the historical pattern is “Nigeria’s oil economics fuel deadly protests” (CNN January 11th, 2012).
While scrolling through all the past events, I notice that Egypt revolution that started as subsidies on bread were lifted leading to student riots, death of a student and then mass riots, and much more.
On October 1st the Economist publishes “..protesting the lifting of fuel subsidies has left dozens of people dead in the capital, Khartoum, and around the country… single bullet, however, that hit a 26-year-old pharmacist in the chest during a protest in Buri… sent shock waves through the heart of Mr Bashir’s regime… The student-led protests are also expected to continue.”
The machine learning algorithm, that enabled this prediction is learning about the future by generalizing sequences of historical events extracted from massive amounts of data points, including: 150 years of NYT articles, millions of web searches, and dynamic webpages. Using unstructured, open ontologies (e.g. Wikipedia,) the approach models the context-conditional probabilities of potential outcomes, by “reading the news” and finding patterns in them.
I believe that generalizing this approach by combining it with other social media reports across several languages and the many internal data sources many of the governments and intelligence offices collect will help bring much higher automation and predictability to the task of predicting civil unrest across large