Jose Cadena

'Beating the news' with EMBERS: forecasting civil unrest using open source indicators

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 June 2013 protests in Brazil and Feb 2014 violent protests in Venezuela. 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.

Forecasting Social Unrest Using Activity Cascades

Social unrest is endemic in many societies, and recent news has drawn attention to happenings in Latin America, the Middle East, and Eastern Europe. Civilian populations mobilize, sometimes spontaneously and sometimes in an organized manner, to raise awareness of key issues or to demand changes in governing or other organizational structures. It is of key interest to social scientists and policy makers to forecast civil unrest using indicators observed on media such as Twitter, news, and blogs. We present an event forecasting model using a notion of activity cascades in Twitter (proposed by Gonzalez-Bailon et al., 2011) to predict the occurrence of protests in three countries of Latin America: Brazil, Mexico, and Venezuela. The basic assumption is that the emergence of a suitably detected activity cascade is a precursor or a surrogate to a real protest event that will happen “on the ground.” Our model supports the theoretical characterization of large cascades using spectral properties and uses properties of detected cascades to forecast events. Experimental results on many datasets, including the recent June 2013 protests in Brazil, demonstrate the effectiveness of our approach.

Detecting and forecasting domestic political crises: a graph-based approach

Forecasting a domestic political crisis (DPC) in a country of interest is a very useful tool for social scientists and policy makers. A wealth of event data is now available for historical as well as prospective analysis. Using the publicly available GDELT dataset, we illustrate the use of frequent subgraph mining to identify signatures preceding DPCs, and the predictive utility of these signatures through both qualitative and quantitative results.

Combining Heterogeneous Data Sources for Civil Unrest Forecasting

Detecting and forecasting civil unrest events (protests, strikes, etc.) is of key interest to social scientists and policy makers because these events can lead to significant societal and cultural changes. We analyze protest dynamics in six countries of Latin America on a daily level, from November 2012 through August 2014, using multiple data sources that capture social, political and economic contexts within which civil unrest occurs. We use logistic regression models with Lasso to select a sparse feature set from our diverse datasets, in order to predict the probability of occurrence of civil unrest events in these countries. The models contain predictors extracted from social media sites (Twitter and blogs) and news sources, in addition to volume of requests to Tor, a widely-used anonymity network. Two political event databases and country-specific exchange rates are also used. Our forecasting models are evaluated using a Gold Standard Report (GSR), which is compiled by an independent group of social scientists and experts on Latin America. The experimental results, measured by F1-scores, are in the range 0.68 to 0.95, and demonstrate the efficacy of using a multi-source approach for predicting civil unrest. Case studies illustrate the insights into unrest events that are obtained with our methods.

On Dense Subgraphs in Signed Network Streams

Signed networks remain relatively under explored despite the fact that many real networks are of this kind. Here, we study the problem of subgraph density in signed networks and show connections to the event detection task. Notions of density have been used in prior studies on anomaly detection, but all existing methods have been developed for unsigned networks. We develop the first algorithms for finding dense subgraphs in signed networks using semi-definite programming based rounding. We give rigorous guarantees for our algorithms, and develop a heuristic EGOSCAN which is significantly faster. We evaluate the performance of EGOSCAN for different notions of density, and observe that it performs significantly better than natural adaptations of prior algorithms for unsigned networks. In particular, the improvement in edge density over previous methods is as much as 85% and usually over 50%. These results are consistent across signed and unsigned networks in different domains. The improvement in performance is even more significant for a constrained version of the problem involving finding subgraphs containing a subset of query nodes. We also develop an event detection method for signed and unsigned networks based on subgraph density. We apply this to three different temporal datasets, and show that our method based on EGOSCAN significantly outperforms existing approaches and baseline methods in terms of the precision-recall tradeoff (by as much as 25-50% in some instances).

An integrated agent-based approach for modeling disease spread in large populations to support health informatics

Disease spread has a much broader impact on public health than the important issues of illnesses and deaths. Among these are chronic health problems, reductions in national wealth, government instability, and crime. Here, we describe an integrated approach for computational health informatics that includes individual-based population construction, and agent-based modeling of dynamics. We restrict dynamics modeling to epidemiology. We itemize technical challenges and provide a case study of the Ebola outbreak in Monrovia, Liberia, with discussion of mobile treatment centers.

Multi-source models for civil unrest forecasting

Civil unrest events (protests, strikes, and “occupy” events) range from small, nonviolent protests that address specific issues to events that turn into large-scale riots. Detecting and forecasting these events is of key interest to social scientists and policy makers because they can lead to significant societal and cultural changes. We forecast civil unrest events in six countries in Latin America on a daily basis, from November 2012 through August 2014, using multiple data sources that capture social, political and economic contexts within which civil unrest occurs. The models contain predictors extracted from social media sites (Twitter and blogs) and news sources, in addition to volume of requests to Tor, a widely used anonymity network. Two political event databases and country-specific exchange rates are also used. Our forecasting models are evaluated using a Gold Standard Report, which is compiled by an independent group of social scientists and subject matter experts. We use logistic regression models with Lasso to select a sparse feature set from our diverse datasets. The experimental results, measured by F1-scores, are in the range 0.68–0.95, and demonstrate the efficacy of using a multi-source approach for predicting civil unrest. Case studies illustrate the insights into unrest events that are obtained with our method. The ablation study demonstrates the relative value of data sources for prediction. We find that social media and news are more informative than other data sources, including the political event databases, and enhance the prediction performance. However, social media increases the variation in the performance metrics.

Context-aided analysis of community evolution in networks

We are interested in detecting and analyzing global changes in dynamic networks (networks that evolve with time). More precisely, we consider changes in the activity distribution within the network, in terms of density (ie, edge existence) and intensity (ie, edge weight). Detecting change in local properties, as well as individual measurements or metrics, has been well studied and often reduces to traditional statistical process control. In contrast, detecting change in larger scale structure of the network is more challenging and not as well understood. We address this problem by proposing a framework for detecting change in network structure based on separate pieces: a probabilistic model for partitioning nodes by their behavior, a label-unswitching heuristic, and an approach to change detection for sequences of complex objects. We examine the performance of one instantiation of such a framework using mostly previously available pieces. The dataset we use for these investigations is the publicly available New York City Taxi and Limousine Commission dataset covering all taxi trips in New York City since 2009. Using it, we investigate the evolution of an ensemble of networks under different spatiotemporal resolutions. We identify the community structure by fitting a weighted stochastic block model. We offer insights on different node ranking and clustering methods, their ability to capture the rhythm of life in the Big Apple, and their potential usefulness in highlighting changes in the underlying network structure.