Cutting Through the Noise: Predictors of Successful Online Message Retransmission in the First 8 Months of the COVID-19 Pandemic.

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.

Retweeting Risk Communication: The Role of Threat and Efficacy.

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.