The Things We Fear. Combining Automated and Manual Content Analysis to Uncover Themes, Topics and Threats in Fear-Related News

Abstract

Terrorism, poverty, cancer: Citizens fear many things. Some of these fears are fueled by fear-related news as a form of sensational media coverage. As research on the variety of fears depicted in the news is scarce, this study analyzes the construction of fear-related news in the US and the UK from 1990 to 2017. By combining unsupervised machine learning in the form of topic modeling (N=15,487) and manual content analysis (N=1013), it explores the prevalence of themes and topics. It also analyzes the fear-inducing presentation of news through the use of fear appeals, specifically which (severe) threats are emphasized by the media. Results indicate that the media do not only concentrate on fears of violence and crime, but also on fears of economic downturn, political unrest, or social fears concerning unemployment. The most prominent threat emphasized across topics is death, followed by political and economic threats. Topic prevalence is highly volatile and coverage has not become more fear-inducing over time. Overall, this study contributes to a better understanding of how news media may foster fear: through mirroring a variety of economic, political and social fears and emphasizing specific threats across them.

Publication
Journalism Studies