Chris J. Kuhlman

'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.

A Study of Information Diffusion over a Realistic Social Network Model

Sociological models of human behavior can explain population-level phenomena within social systems; computer modeling can simulate a wide variety of scenarios and allow one to pose and test hypotheses about the social system. In this paper, we model and examine the spread of information through personal conversations in a simulated socio-technical network that provides a high degree of realism and a great deal of captured detail. To our knowledge thisis the first time information spread via conversation has been modeled against a statistically accurate simulation of peoples daily interactions within a specific urban or rural environment, capturing the points in time and space at which two people could converse, and providing a realistic basis formodeling human behavior during face-to-face interaction.We use a probabilistic model to decide whether two people will converse about a particular topic based on their similarity and familiarity. Similarity is modeled by matching selected demographic characteristics, while familiarity is modeled by the amount of contact required to convey information. We report our findings on the effects of familiarity and similarity on the spread of information over the social network. We resolve the results by age group, daily activities, time, household income, household size and examine the relative effect of these factors.For informal topics where little familiarity is required, shopping and recreational activities predominate; otherwise, home, work, and school predominate. We find that youths play a significant role in spreading information through a community rapidly, mainly through interactions in schools and recreational activities.

Blocking Simple and Complex Contagion by Edge Removal

Eliminating interactions among individuals is an important means of blocking contagion spread, e.g., closing schools during an epidemic or shutting down electronic communication channels during social unrest. We study contagion blocking in networked populations by identifying edges to remove from a network, thus blocking contagion transmission pathways. We formulate various problems to minimize contagion spread and show that some are efficiently solvable while others are formally hard. We also compare our hardness results to those from node blocking problems and show interesting differences between the two. Our main problem is not only hard, but also has no approximation guarantee, unless P=NP. Therefore, we devise a heuristic for the problem and compare its performance to state-of-the-art heuristics from the literature. We show, through results of 12 (network, heuristic) combinations on three real social networks, that our method offers considerable improvement in the ability to block contagions in weighted and unweighted networks. We also conduct a parametric study to understand the limitations of our approach.

Finding critical nodes for inhibiting diffusion of complex contagions in social networks

We study the problem of inhibiting diffusion of complex contagions such as rumors, undesirable fads and mob behavior in social networks by removing a small number of nodes (called critical nodes) from the network. We show that, in general, for any ρ ≥ 1, even obtaining a ρ-approximate solution to these problems is NP-hard. We develop efficient heuristics for these problems and carry out an empirical study of their performance on three well known social networks, namely epinions, wikipedia and slashdot. Our results show that the heuristics perform well on the three social networks.

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.

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.

Interaction-based HPC modeling of social, biological, and economic contagions over large networks

Modeling large-scale stochastic systems of heterogeneous individuals and their interactions, where multiple behaviors and contagions co-evolve with multiple interaction networks, requires high performance computing and agent-based simulations. We present graph dynamical systems as a formalism to reason about network dynamics and list phenomena from several application domains that have been modeled as graph dynamical systems to demonstrate its wide-ranging applicability. We describe and contrast three tools developed in our laboratory that use this formalism to model these systems. Beyond evaluating system dynamics, we are interested in understanding how to control contagion processes using resources both endogenous and exogenous to the system being investigated to support public policy decision-making. We address control methods, such as interventions, and provide illustrative simulation results.

Inhibiting diffusion of complex contagions in social networks: theoretical and experimental results

We consider the problem of inhibiting undesirable contagions (e.g. rumors, spread of mob behavior) in social networks. Much of the work in this context has been carried out under the 1-threshold model, where diffusion occurs when a node has just one neighbor with the contagion. We study the problem of inhibiting more complex contagions in social networks where nodes may have thresholds larger than 1. The goal is to minimize the propagation of the contagion by removing a small number of nodes (called critical nodes) from the network. We study several versions of this problem and prove that, in general, they cannot even be efficiently approximated to within any factor

CINET: A cyberinfrastructure for network science

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