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.
|Original language||English (US)|
|Number of pages||12|
|Journal||Annals of the American Academy of Political and Social Science|
|State||Published - May 15 2015|
Bibliographical notePublisher Copyright:
© 2015 by The American Academy of Political and Social Science
Copyright 2015 Elsevier B.V., All rights reserved.
- computational content analysis
- computational social science
- content analysis
- digital research methods
- hybrid content analysis
- media analysis