Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2808797.2808847 | Advances in Social Network Analysis and Mining |
Keywords | Field | DocType |
heterogeneous data sources,civil unrest event forecasting,civil unrest event detection,societal change,cultural change,protest dynamics analysis,Latin America,multiple data sources,social context,political context,economic context,logistic regression models,sparse feature set selection,occurrence probability prediction,social media sites,Twitter,blogs,Tor,anonymity network,political event databases,country-specific exchange rates,Gold Standard Report,GSR,F1-scores,multisource approach | Data mining,Crowds,Latin Americans,Social media,Sociology,Lasso (statistics),Emerging markets,Anonymity,Unrest,Politics | Conference |
Citations | PageRank | References |
9 | 0.60 | 17 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gizem Korkmaz | 1 | 98 | 11.10 |
| Jose Cadena | 2 | 98 | 7.53 |
| Chris J. Kuhlman | 3 | 216 | 25.03 |
| Achla Marathe | 4 | 203 | 23.77 |
| Anil Kumar S. Vullikanti | 5 | 1135 | 98.30 |
| Naren Ramakrishnan | 6 | 1913 | 176.25 |