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Crowdsourcing the Measurement of Interstate Conflict

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Listed:
  • Vito D’Orazio
  • Michael Kenwick
  • Matthew Lane
  • Glenn Palmer
  • David Reitter

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.

Suggested Citation

  • Vito D’Orazio & Michael Kenwick & Matthew Lane & Glenn Palmer & David Reitter, 2016. "Crowdsourcing the Measurement of Interstate Conflict," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-21, June.
    • Handle: RePEc:plo:pone00:0156527
    DOI: 10.1371/journal.pone.0156527
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    References listed on IDEAS

    as
    1. D'Orazio, Vito & Landis, Steven T. & Palmer, Glenn & Schrodt, Philip, 2014. "Separating the Wheat from the Chaff: Applications of Automated Document Classification Using Support Vector Machines," Political Analysis, Cambridge University Press, vol. 22(2), pages 224-242, April.
    2. repec:cup:judgdm:v:5:y:2010:i:5:p:411-419 is not listed on IDEAS
    3. Seth Cooper & Firas Khatib & Adrien Treuille & Janos Barbero & Jeehyung Lee & Michael Beenen & Andrew Leaver-Fay & David Baker & Zoran Popović & Foldit players, 2010. "Predicting protein structures with a multiplayer online game," Nature, Nature, vol. 466(7307), pages 756-760, August.
    4. King, Gary & Lowe, Will, 2003. "An Automated Information Extraction Tool for International Conflict Data with Performance as Good as Human Coders: A Rare Events Evaluation Design," International Organization, Cambridge University Press, vol. 57(3), pages 617-642, July.
    5. Berinsky, Adam J. & Huber, Gregory A. & Lenz, Gabriel S., 2012. "Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk," Political Analysis, Cambridge University Press, vol. 20(3), pages 351-368, July.
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    Citations

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    Cited by:

    1. Glenn Palmer & Roseanne W McManus & Vito D’Orazio & Michael R Kenwick & Mikaela Karstens & Chase Bloch & Nick Dietrich & Kayla Kahn & Kellan Ritter & Michael J Soules, 2022. "The MID5 Dataset, 2011–2014: Procedures, coding rules, and description," Conflict Management and Peace Science, Peace Science Society (International), vol. 39(4), pages 470-482, July.
    2. Juan D Botero & Weisi Guo & Guillem Mosquera & Alan Wilson & Samuel Johnson & Gicela A Aguirre-Garcia & Leonardo A Pachon, 2019. "Gang confrontation: The case of Medellin (Colombia)," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-19, December.
    3. Michael R Kenwick & Beth A Simmons & Richard J McAlexander, 2024. "Infrastructure and authority at the state’s edge: The Border Crossings of the World dataset," Journal of Peace Research, Peace Research Institute Oslo, vol. 61(3), pages 500-510, May.
    4. Zhanna Terechshenko, 2020. "Hot under the collar: A latent measure of interstate hostility," Journal of Peace Research, Peace Research Institute Oslo, vol. 57(6), pages 764-776, November.

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