• 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. • Copy link Email X LinkedIn Facebook Line Reddit QZone Sina Weibo WeChat WhatsApp 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-sciencetest-of-time awardfor a 2014 paper titled “‘Beating the news’ with EMBERS: Forecasting civil unrest using open-source indicators”. • 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 ofpublic protests in Brazilin 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.

Article Summaries:

  • 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”. 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 mo

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