Computational Methods for Analyzing Health News Coverage

Researchers that investigate the media’s coverage of health have historically relied on keyword searches to retrieve relevant health news coverage, and manual content analysis methods to categorize and score health news text. These methods are problematic. Manual content analysis methods are labor intensive, time consuming, and inherently subjective because they rely on human coders to review, score, and annotate content. Retrieving relevant health news coverage using keywords can be challenging because manually defining an optimal keyword query, especially for complex health topics and media analysis concepts, can be very difficult, and the optimal query may vary based on when the news was published, the type of news published, and the target audience of the news coverage. This dissertation research investigated computational methods that can assist health news investigators by facilitating these tasks. The first step was to identify the research methods currently used by investigators, and the research questions and health topics researchers tend to investigate. To capture this information an extensive literature review of health news analyses was performed. No literature review of this type and scope could be found in the research literature. This review confirmed that researchers overwhelmingly rely on manual content analysis methods to analyze the text of health news coverage, and on the use of keyword searching to identify relevant health news articles. To investigate the use of computational methods for facilitating these tasks, classifiers that categorize health news on relevance to the topic of obesity, and on their news framing were developed and evaluated. The obesity news classifier developed for this dissertatio