Content Analysis in an Era of Big Data: A hybrid approach to computational and manual methods

Abstract

Massive datasets of communication are challenging traditional, human-driven approaches to content analysis. Computational methods present enticing solutions to these problems but in many cases are insufficient on their own. We argue that an approach blending computational and manual methods throughout the content analysis process may yield more fruitful results, and draw on a case study of news sourcing on Twitter to illustrate this hybrid approach in action. Careful combinations of computational and manual techniques can preserve the strengths of traditional content analysis, with its systematic rigor and contextual sensitivity, while also maximizing the large-scale capacity of Big Data and the algorithmic accuracy of computational methods.

Rodrigo Zamith

Associate Professor

My research and teaching interests lie at the intersection of journalism and technology, with a focus on the reconfiguration of journalism in a changing media environment and the development of digital research methods for social scientists.