Computer-assisted content analysis of newspapers

Abstract

Content analysis as a methodology of data collection from text sources is very labor intensive and therefore costly. Advances in computer-assisted content analysis have greatly increased the power of the methodology. But, even in a computer environment, complex searches require extensive prior manipulation of the original text in order to assign to it a basic structure. It is this structure that allows researchers to perform complex queries in highly general ways. I argue that Semantics provides powerful templates for structuring text data (text grammars) and other conceptual tools that can be profitably used to increase the efficiency of content analysis projects (e.g. news schema and macro propositions). More generally, I argue that efficiency in content analysis projects depends upon such characteristics as (1) organization of the coding task; (2) newspaper reporting practices. The discussion of these characteristics results in several suggestions on how to increase the efficiency of the methodology.