Crowdsourcing the Measurement of Interstate Conflict.
Much of the data used to measure conflict is extracted from news reports. This is typically accomplished using either expert coders to quantify the relevant information or machine coders to automatically extract data from documents. Although expert coding is costly, it produces quality data. Machine coding is fast and inexpensive, but the data are noisy. To diminish the severity of this tradeoff, we introduce a method for analyzing news documents that uses crowdsourcing, supplemented with computational approaches. The new method is tested on documents about Militarized Interstate Disputes, and its accuracy ranges between about 68 and 76 percent. This is shown to be a considerable improvement over automated coding, and to cost less and be much faster than expert coding.
School of Economic, Political, and Policy Sciences, University of Texas at Dallas, Richardson, TX, United States of America.
Vito
V
D'Orazio
Department of Political Science, The Pennsylvania State University, University Park, PA, United States of America.
Michael
M
Kenwick
Department of Political Science, The Pennsylvania State University, University Park, PA, United States of America.
Matthew
M
Lane
Department of Political Science, The Pennsylvania State University, University Park, PA, United States of America.
Glenn
G
Palmer
College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA, United States of America.
David
D
Reitter
D000068337:Armed Conflicts; D063045:Crowdsourcing / Q000191:economics / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D017008:Negotiating