Why a 12-year-old forecasting paper has stood the test of time
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Machine learning
Why a 12-year-old forecasting paper has stood the test of time
Amazon Scholar Aravind Srinivasan coauthored a 2014 paper about forecasting civil unrest in Latin America, which won a test-of-time award at KDD 2025.
By Larry Hardesty
February 17, 2026
3 min read
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At the 2025 meeting of the Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining (KDD), Amazon Scholar and University of Maryland professor Aravind Srinivasan was one of 30 coauthors from eight institutions to win the conference’s applied-data-science test-of-time award for a 2014 paper titled “‘ Beating the news’ with EMBERS: Forecasting civil unrest using open-source indicators ”.
Amazon Scholar Aravind Srinivasan is a distinguished university professor and professor of computer science at the University of Maryland, College Park. His research interests lie at the intersection of algorithms, continuous and combinatorial optimization, and machine learning. Credit: University of Maryland, College Park
In those days before the predominance of neural networks, EMBERS (for “early model-based event recognition using surrogates”) was a collection of five machine learning models that used techniques like Bayesian classification and logistic regression to process publicly available information such as social-media posts, news reports, blog posts, economic indicators, and satellite imagery and predict the likelihood of civil unrest in 10 Latin American nations. When the paper was published, EMBERS had been in operation for two years and had correctly predicted the surge and subsidence of public protests in Brazil in mid-2013. Amazon Science caught up with Srinivasan to discuss the EMBERS paper and the reasons it continues to draw attention.
Q. What was the focus of the paper?
A. The paper focuses on Latin America and on four types of events — broadly, financial events, election outcomes, health events, and social unrest. If you have access to newspaper data, Internet data, satellite imaging — because if hospital parking lots are crowded, it suggests something about health events — and so on, given all this open-source intelligence, can we make intelligent forecasts, along with probabilities, about events happening over the next several weeks or months? How do we evaluate them intelligently, match up the warnings that we issued with the events that actually happened? And finally, since we run multiple algorithms with complementary strengths, how do we fuse them intelligently using Bayesian reasoning in order to issue our alerts?
Q. What are the aspects of the paper that have stood the test of time?
A. We used the machine learning of 12, 13 years ago, which has been greatly surpassed, so I think that has been less of a contribution. But if you have some machine learning model, you need to be able to validate it — if it's a supervised-learning…
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Notability
notability 2.0/10Old paper recap, low industry impact.