Automated content analysis and crisis communication research

Communication plays a central role in how crisis events evolve. The huge collection of today's digital available content from actors such as organizations, news media, and the public provides scholars with the opportunity to analyze large-sized collections of crisis-related communication and provide supplementary evidence for previous findings from smaller scaled research. However, the massive costs and complexity of analyzing these large-scaled data sets have hindered their use within the field of crisis research. This paper aims to provide an overview of how automated content analysis can potentially simplify and complement the analysis of these large collections of texts. Computational methods have long been used in the field of computer science and are currently gaining momentum within the field of crisis communication. This paper discusses the dictionary method, supervised method, and the unsupervised method as potential useful tools for analyzing crisis communication.