Basic Information Retrieval for Content Analysis
Chris J. Vargo · 2024
This chapter explores basic information-retrieval techniques for content analysis, focusing on static datasets typical in academic research. It discusses the definition and challenges of “big data” in content analysis, emphasizing the importance of appropriate sampling and the limitations of computational methods. The chapter also details a case study on Twitter data analysis concerning political incivility, illustrating the use of simple classifiers and the importance of intercoder reliability. It concludes by highlighting the potential of marrying different datasets to enhance content analysis, advocating for a pragmatic approach to computational methods in academic research.