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Content Analysis and the Algorithmic Coder

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  • Rodrigo Zamith
  • Seth C. Lewis

Abstract

To deal with ever-larger datasets, media scholars are increasingly using computational analytic methods. This article focuses on how the traditional (manual) approach to conducting a content analysis—a primary method in the study of media messages—is being reconfigured, assesses what is gained and lost in turning to computational solutions, and builds on a “hybrid†approach to content analysis. We argue that computational methods are most fruitful when variables are readily identifiable in texts and when source material is easily parsed. Manual methods, though, are most appropriate for complex variables and when source material is not well digitized. These modes can be effectively combined throughout the process of content analysis to facilitate expansive and powerful analyses that are reliable and meaningful.

Suggested Citation

  • Rodrigo Zamith & Seth C. Lewis, 2015. "Content Analysis and the Algorithmic Coder," The ANNALS of the American Academy of Political and Social Science, , vol. 659(1), pages 307-318, May.
  • Handle: RePEc:sae:anname:v:659:y:2015:i:1:p:307-318
    DOI: 10.1177/0002716215570576
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    1. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    2. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    3. Grimmer, Justin, 2010. "A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases," Political Analysis, Cambridge University Press, vol. 18(1), pages 1-35, January.
    4. Daniel J. Hopkins & Gary King, 2010. "A Method of Automated Nonparametric Content Analysis for Social Science," American Journal of Political Science, John Wiley & Sons, vol. 54(1), pages 229-247, January.
    5. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    6. Steven Ruggles, 2014. "Big Microdata for Population Research," Demography, Springer;Population Association of America (PAA), vol. 51(1), pages 287-297, February.
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