Name
Abstract | ||
|---|---|---|
Agent-based simulation models are an important methodology for explaining social behavior and forecasting social change. However, a major drawback to using such models is that they are difficult to instantiate for specific cases and so are rarely reused. We describe a text-mining network analytic approach for rapidly instantiating a model for predicting the tendency toward revolution and violence based on social and cultural characteristics of a large collection of actors. We illustrate our approach using an agent-based dynamic network framework, Construct, and newspaper data for the 16 countries associated with the Arab Spring. We assess the overall accuracy of the base model across independent runs for 20 different months during the Arab Spring, observing that although predictions led to several false positives, the model is able to predict revolution before it occurs in three of the four nations in which the government was successfully overthrown. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s13278-014-0177-5 | Social Netw. Analys. Mining |
Keywords | Field | DocType |
Agent-based modeling, Social simulation, Model instantiation, Arab Spring | Drawback,Dynamic network analysis,Data science,Computer science,Operations research,Simulation modeling,Social simulation,Newspaper,Social change,False positive paradox,Government | Journal |
Volume | Issue | ISSN |
4 | 1 | 1869-5469 |
Citations | PageRank | References |
1 | 0.34 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kenneth Joseph | 1 | 9 | 5.91 |
| Kathleen M. Carley | 2 | 2507 | 270.10 |
| David Filonuk | 3 | 1 | 0.34 |
| Geoffrey P. Morgan | 4 | 1 | 0.68 |
| Jürgen Pfeffer | 5 | 1 | 0.34 |