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Catastrophic cyber incidents—events of low probability but high impact, with the potential to incur billions of dollars in damages—are prompting insurers to elevate premiums, create higher barriers for potential buyers, and tighten policies with exclusions. While these responses of the insurance industry are important to prevent its insolvency during catastrophic incidents due to excessive claims, they lead to a notable gap in market protection. Using a content analysis of multistakeholder comments submitted in response to a Treasury Department Request for Information (RFI), this study seeks to define what constitutes a catastrophic cyber event, identify mitigation strategies, evaluate the current capacity of the cyber insurance sector to handle such incidents, and investigate the potential roles and support mechanisms that the government can provide to enhance the insurance sector's capacity to manage these extreme risks. This paper is one of the pioneering studies using data and a multistakeholder perspective to provide essential guidance for policymakers, regulators, the insurance industry, and the cybersecurity sector in formulating robust policies and strategies to address catastrophic cyber risks, ultimately enhancing national economic and technological resilience. [ABSTRACT FROM AUTHOR]

In this paper, we investigate how message construction, style, content, and the textual content of embedded images impacted message retransmission over the course of the first 8 months of the coronavirus disease 2019 (COVID-19) pandemic in the United States. We analyzed a census of public communications (n = 372,466) from 704 public health agencies, state and local emergency management agencies, and elected officials posted on Twitter between January 1 and August 31, 2020, measuring message retransmission via the number of retweets (ie, a message passed on by others), an important indicator of engagement and reach. To assess content, we extended a lexicon developed from the early months of the pandemic to identify key concepts within messages, employing it to analyze both the textual content of messages themselves as well as text included within embedded images (n = 233,877), which was extracted via optical character recognition. Finally, we modelled the message retransmission process using a negative binomial regression, which allowed us to quantify the extent to which particular message features amplify or suppress retransmission, net of controls related to timing and properties of the sending account. In addition to identifying other predictors of retransmission, we show that the impact of images is strongly driven by content, with textual information in messages and embedded images operating in similar ways. We offer potential recommendations for crafting and deploying social media messages that can "cut through the noise" of an infodemic. [ABSTRACT FROM AUTHOR]