Social media platforms like Twitter and Facebook provide risk communicators with the opportunity to quickly reach their constituents at the time of an emerging infectious disease. On these platforms, messages gain exposure through message passing (called "sharing" on Facebook and "retweeting" on Twitter). This raises the question of how to optimize risk messages for diffusion across networks and, as a result, increase message exposure. In this study we add to this growing body of research by identifying message-level strategies to increase message passing during high-ambiguity events. In addition, we draw on the extended parallel process model to examine how threat and efficacy information influence the passing of Zika risk messages. In August 2016, we collected 1,409 Twitter messages about Zika sent by U.S. public health agencies' accounts. Using content analysis methods, we identified intrinsic message features and then analyzed the influence of those features, the account sending the message, the network surrounding the account, and the saliency of Zika as a topic, using negative binomial regression. The results suggest that severity and efficacy information increase how frequently messages get passed on to others. Drawing on the results of this study, previous research on message passing, and diffusion theories, we identify a framework for risk communication on social media. This framework includes four key variables that influence message passing and identifies a core set of message strategies, including message timing, to increase exposure to risk messages on social media during high-ambiguity events.

As the most visible face of health expertise to the general public, health agencies have played a central role in alerting the public to the emerging COVID-19 threat, providing guidance for protective action, motivating compliance with health directives, and combating misinformation. Social media platforms such as Twitter have been a critical tool in this process, providing a communication channel that allows both rapid dissemination of messages to the public at large and individual-level engagement. Message dissemination and amplification is a necessary precursor to reaching audiences, both online and off, as well as inspiring action. Therefore, it is valuable for organizational risk communication to identify strategies and practices that may lead to increased message passing among online users. In this research, we examine message features shown in prior disasters to increase or decrease message retransmission under imminent threat conditions to develop models of official risk communicators’ messages shared online from February 1, 2020-April 30, 2020. We develop a lexicon of keywords associated with risk communication about the pandemic response, then use automated coding to identify message content and message structural features. We conduct chi-square analyses and negative binomial regression modeling to identify the strategies used by official risk communicators that respectively increase and decrease message retransmission. Findings show systematic changes in message strategies over time and identify key features that affect message passing, both positively and negatively. These results have the potential to aid in message design strategies as the pandemic continues, or in similar future events.

The current study examines crisis communication on social media by observing how twelve National Weather Service (NWS) offices use Twitter to facilitate engagement with stakeholders during threat and nonthreat periods. Using content analytic methods, we examine message features related to content and structure during a 3‐month period in spring 2016. We conduct chi‐square analyses to determine how the prevalence of these features varies by time. Results indicate that NWS offices use Twitter for ongoing engagement communication through both the content and structure of their messages, where community building and action‐orientated messages are primarily used during nonthreat periods. When fair weather changes and storms approach, the offices shift to the communication of risk. Our findings underscore the need to examine organizational communication practices in a manner that recognizes the distinctions between long‐term engagement goals and short‐term goals related to the mission of the organization.

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.

In this paper, we capture, identify, and describe the patterns of longitudinal risk communication from public health communicating agencies on Twitter during the first 60 days of the response to the novel coronavirus disease 2019 (COVID-19) pandemic. We collected 138,546 tweets from 696 targeted accounts from February 1 to March 31, 2020, employing term frequency-inverse document frequency to identify keyword hashtags that were distinctive on each day. Our team conducted inductive content analysis to identify emergent themes that characterize shifts in public health risk communication efforts. As a result, we found 7 distinct periods of communication in the first 60 days of the pandemic, each characterized by a differing emphasis on communicating information, individual and collection action, sustaining motivation, and setting social norms. We found that longitudinal risk communication in response to the COVID-19 pandemic shifted as secondary threats arose, while continuing to promote pro-social activities to reduce impact on vulnerable populations. Identifying patterns of risk communication longitudinally allows public health communicators to observe changes in topics and priorities. Observations from the first 60 days of the COVID-19 pandemic prefigures ongoing messaging needs for this event and for future disease outbreaks.