Combining Heterogeneous Data Sources for Civil Unrest Forecasting

Abstract

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.

Activism via attention: interpretable spatiotemporal learning to forecast protest activities

“…There have been studies that utilize the spatial, temporal or spatiotemporal dependencies in modeling or predicting the events. Several studies employed logistic regression or heuristics to forecast/detect events from social media related to anomalies [20,21], crime [22] and civil unrest [23,24]. Cadena et al [25] proposed an event forecasting model for civil unrest that uses a notion of activity cascades derived from the Twitter communication networks.…”

Predicting Social Unrest Events with Hidden Markov Models Using GDELT

“…Event prediction has been explored in a variety of applications, including elections [20,21], disease outbreaks [22], stock market movements [23,24], social unrest event prediction [11,12,[25][26][27][28][29][30][31], movie earnings [23], crime [32], and failure prediction [33]. Most recent social unrest event prediction techniques can be categorized into three types: planned event forecasting, classification based prediction, and time series mining.…”

“…Existing works attempted to use linear regression [8], time series forecasting [9], and frequent subgraphs [10,11] to conduct the prediction work using GDELT. In [12], GDELT and ICEWS are used as data sources to predict unrest in Latin America. Nevertheless, in these works comparatively little attention has been paid to consider the event development stages in the forecasting models with GDELT.…”

Full Meta Object profiling for flexible geoprocessing workflows

“…GDELT draws its observations from textual analysis of international news coverage, which is automatically processed to create coded observations. The dataset is used for global-scale political monitoring and prediction and has, for example, been adopted for forecasting civil unrest (Korkmaz et al, 2015 ) and monitoring sentiment toward political ideas or events (Bodas-Sagi & Labeaga, 2016 ).…”