Content Analysis and the Algorithmic Coder: What Computational Social Science Means for Traditional Modes of Media Analysis. Issue 1 (May 2015)

• Record Type: Journal Article
• Authors: Zamith, Rodrigo; Lewis, Seth C.
• Editors: Shah, Dhavan V.; Cappella, Joseph N.; Neuman, W. Russell
• Is Part Of: Annals of the American Academy of Political and Social Science. Volume 659:Issue 1(2015:May)
• Page Start: 307
• Page End: 318
• Publication Date: 2015-05
• Subjects: content analysis -- computational social science -- computational content analysis -- hybrid content analysis -- digital research methods -- media analysis -- algorithms; Social sciences -- Periodicals; Social sciences -- United States -- Periodicals; Political science -- Periodicals; United States -- Politics and government -- Periodicals; 300
• DOI: 10.1177/0002716215570576
• Languages: English
• ISSNs: 0002-7162

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.