Warning tweets: serial transmission of messages during the warning phase of a disaster event

TLDR

Serial transmission, the passing of information from one source to another, is a key phenomenon in informal communication during emergencies, and Twitter enables large‑scale study of the factors that influence message retransmission. The study examines how message content, style, and public attention affect the likelihood that individuals retweet official organization tweets during a disaster. Researchers collected all public tweets from official government accounts during a 48‑hour period of the Waldo Canyon wildfire, coded them for thematic content and style, and built predictive models linking these features and follower changes to retweet behavior. The models identified the key elements that drive public retransmission in the emergency phase, offering strategies for designing and disseminating messages through networked social media during imminent threat.

Abstract

Serial transmission – the passing on of information from one source to another – is a phenomenon of central interest in the study of informal communication in emergency settings. Microblogging services such as Twitter make it possible to study serial transmission on a large scale and to examine the factors that make retransmission of messages more or less likely. Here, we consider factors predicting serial transmission at the interface of formal and informal communication during disaster; specifically, we examine the retransmission by individuals of messages (tweets) issued by formal organizations on Twitter. Our central question is the following: How do message content, message style, and public attention to tweets relate to the behavioral activity of retransmitting (i.e. retweeting) a message in disaster? To answer this question, we collect all public tweets sent by a set of official government accounts during a 48-hour period of the Waldo Canyon wildfire. We manually code tweets for their thematic content and elements of message style. We then create predictive models to show how thematic content, message style, and changes in number of Followers affect retweeting behavior. From these predictive models, we identify the key elements that affect public retransmission of messages during the emergency phase of an unfolding disaster. Our findings suggest strategies for designing and disseminating messages through networked social media under periods of imminent threat.