Work

Crowdsourcing the Measurement of Interstate Conflict

Year: 2016

Type: article

Abstract: 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.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0156527

Authors: Vito D’Orazio, Michael Kenwick, Matthew Lane, Glenn Palmer, David Reitter

Institutions: The University of Texas at Dallas, Pennsylvania State University

Language: English

Topic: Computational and Text Analysis Methods

Subfield: General Social Sciences

Field: Social Sciences

Domain: Social Sciences

SDG: Peace, justice, and strong institutions

Open Access status: gold

Estimated APC paid: $2,382