# 'Beating the news' with EMBERS: Forecasting Civil Unrest using Open Source Indicators

arXiv: https://arxiv.org/abs/1402.7035 · HTML: https://mlsanity.com/paper/1402.7035/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2014.

## Built on by (most cited first, 10 of 10)

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- [A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas](https://mlsanity.com/paper/2302.04018/) (2023)
- [A Survey on Societal Event Forecasting with Deep Learning](https://mlsanity.com/paper/2112.06345/) (2021)
- [LaTeX: Language Pattern-aware Triggering Event Detection for Adverse Experience during Pandemics](https://mlsanity.com/paper/2310.03941/) (2023)
- [Natural Language Querying System Through Entity Enrichment](https://mlsanity.com/paper/2410.15753/) (2024)
- [From Seasonality to Semantics: Benchmarking a Hybrid Probabilistic Forecasting System for Roadblocks in Bolivia](https://mlsanity.com/paper/2607.21785/) (2026)

## Abstract (arXiv)

We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future since Nov 2012 which have been (and continue to be) evaluated by an independent T&E team (MITRE). Of note, EMBERS has successfully forecast the uptick and downtick of incidents during the June 2013 protests in Brazil. We outline the system architecture of EMBERS, individual models that leverage specific data sources, and a fusion and suppression engine that supports trading off specific evaluation criteria. EMBERS also provides an audit trail interface that enables the investigation of why specific predictions were made along with the data utilized for forecasting. Through numerous evaluations, we demonstrate the superiority of EMBERS over baserate methods and its capability to forecast significant societal happenings.