Crowdsourcing the Measurement of Interstate Conflict

Vito J. D'Orazio · Michael Kenwick · Glenn Palmer · David Reitter

14 citations0/0 files storedPLoS ONE

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Computational and Text Analysis Methods Political Conflict and Governance Mobile Crowdsensing and Crowdsourcing

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.

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1984Content Analysis: An Introduction to its Methodology.2019Content Analysis: An Introduction to Its Methodology 1968Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit.1971Measuring nominal scale agreement among many raters.2002Content Analysis in Mass Communication: Assessment and Reporting of Intercoder Reliability 1996Assessing agreement on classification tasks: the kappa statistic 1907Vox Populi 1996Militarized Interstate Disputes, 1816–1992: Rationale, Coding Rules, and Empirical Patterns

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2015The MID4 dataset, 2002–2010: Procedures, coding rules and description 2021The MID5 Dataset, 2011–2014: Procedures, coding rules, and description 2019Updating the Militarized Interstate Dispute Data: A Response to Gibler, Miller, and Little 2024Experts, coders and crowds: An analysis of substitutability

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