Cross Media Disaster Discourse Analysis using Computational Text Methods

• In Event: Frontiers of Disaster and Crisis Knowledge

Abstract

When natural disasters strike, media responses serve multiple functions. Media can focus the general public’s attention on a disruptive event, disseminate potentially lifesaving information to those in the disaster area, inform those not in the area of how they can help, provide information to the public on how they can better prepare for a potential similar disaster, and connect disaster outcomes to government policies or private-sector initiatives. However, not all media sources serve the same purpose, nor is every event covered to the same extent. What explains the variation between source coverage and content? How are people both in and out of disaster areas using the variety of news sources? Finally, how does the variation affect society’s social construction of disaster risk? If we assume that risk is not purely objective but embedded in the social structure of society in which media sources are actors, then it is important to understand how media coverage shapes our perceptions of disaster resilience. The relationship between the characteristics of a disaster and the extent of media coverage has not been thoroughly explored in the existing resilience scholarship. This article incorporates data on media coverage of multiple recent natural disaster events such as hurricanes, earthquakes, and wildfires from a variety of media sources including traditional media like newspapers and alternative media sources like social media. The disaster discourse data is analyzed using computational text analysis methods to understand the variation of coverage, content, and usage across the large corpus of media sources and explain how the said variation affects our greater understanding of risk. Society’s construction of risk affects disaster perceptions, public policy prescriptions, private sector initiatives, and civil society solutions that contribute to resilience in an age of destabilization and disruption

Author

• Garrett Morrow, Northeastern University