Content driving exposure and attention to tweets during local, high-impact weather events

Abstract

The use of Twitter to disseminate weather information presents need for the analysis of what types of messages, and specifically warning messages, incur exposure and attention. Having this knowledge could increase exposure and attention to messages and perhaps increase retransmission through Twitter. Two models describe the cognitive processing of tweets and warnings. The extended parallel process model describes components of an effective warning message. Even in a tweet, ignoring one or both critical components of a warning—threat and efficacy—could inhibit a user from taking the correct protective action. The protective action decision model (PADM) describes risk perception and factors that enable or disable one from giving attention to a message. The PADM also helps to define impressions, retweets or likes as metrics of exposure or attention to a tweet. Tweets from three Twitter accounts within one television market during two high-impact weather events were examined. From an individual account, impressions, retweets and likes were collected to identify commonalities to tweets with much exposure and attention. Results indicate photographs and geographically specific messages were popular. Second, from two competing television weather accounts, warning tweet formats were compared to identify exposure and attention to each. Warning tweets providing threat and efficacy performed best. The purpose of this work is twofold. First is to identify local trends to compliment findings from studies with large sample sizes. Second is to apply existing theory on warning message content to Twitter. This approach should benefit communication strategies of key information nodes—local meteorologists—during high-impact weather events.