Warning the public about potential risks and motivating the public to take protective actions is an area that calls for continued scholarly inquiry (Lachlan & Spence, 2007; 2009). Various identification strategies and theoretically based approaches have been used to persuade and motivate the public, particularity in the event of weather-related emergencies (Fishbein & Ajzen, 2010; Miller, Adame & Moore, 2013;). One common feature of large-scale storms which make them distinct from tornadoes, earthquakes, and other natural disasters is their identification by a name.
The naming of such storms may also provide the opportunity for individuals to use heuristic processing to understand the severity and threat of an impending storm. Consequently, it is possible that a more menacing or iconic name may motivate people differently that a more subdued or unemotional name. The current study examines perceptions of threats and the actions of public as a result of a name given to a storm, specifically when viewed on social media.
Exemplification theory is presented as a means of explaining the potential of named storms to influence perceptions (Zillmann 1999, 2002). Exemplars exist on a continuum of how accurately a portrayal represents the larger occurrence, and are likely to drive judgments. The theory draws on evolutionary principles along with three cognitive mechanisms (quantification, representativeness, and availability heuristics), to offer that exemplars that are concrete, iconic, and emotionally arousing influence perceptions more than those are abstract and emotionally inconsequential (Zillmann, 2002; Spence et al, 2016).
To that end, the following research questions are proposed:
RQ1: To what extent does the naming of a story have effects on perceptions of storm severity?
RQ2: To what extent does the naming of a story have effects on perceptions of damage likelihood?
211 participants were recruited from introductory classes at a research university, and randomly assigned to one of three conditions. The procedure was constructed to allow the participants to see a NOAA Twitter feed with one of the conditions. There were no references to the date of the tropical storm; however, there were references to the minutes and hours which passed between each tweet. The first condition contained a storm that was unnamed. It was simply “Tropical Strom.” For example, some tweets would begin with “in preparation for the tropical storm, FEMA and other federal agencies….” The second and third conditions used the name Sam, as it was not seen as emotional, and Sabastian, as it was perceived as emotional and iconic. Thus, the two condition tweets would state “in preparation for the tropical storm [NAME], FEMA and other federal agencies….” The tropical storm was located off the coast of Texas, geographically distant from the respondents, and a tropical storm was used rather than a hurricane as to ensure participants were believing it was a real previous event.
For perceptions of severity, the initial model failed to account for a significant proportion of the variance, F (3,199) = 2.28, n.s. However, the addition of the experimental condition produced a significant model, F (4, 198) = 2.72, p < .03, D R2 = .02. An examination of the standardized regression coefficient for condition indicates b = - .137, p < .05. While accounting for a small amount of variance, an examination of the descriptive statistics indicates that severity perceptions were strongest in the no name condition (M = 7.01, SD = 1.42), followed by the non-exemplar name (M = 6.83, SD = 1.44) and the exemplar name (M = 6.60, SD = 1.52). Older respondents indicated lower perceptions of severity, b = - .154, p < .03.
Similar results were detected for likelihood of damage. The initial model failed to account for a significant amount of variance, F (3,199) = 2.50, n.s. The addition of the condition produced a significant model, F (4, 198) = 2.49, p < .05, D R2 = .02. The standardized coefficient for condition did not achieve significance, though again age negatively predicted perceptions of the likelihood of flood damage, b = - .179, p < .02.
A sleeper effect (although not directly measured) may explain why perceptions of severity and likelihood were highest in the condition where the storm did not have a name (see Westerman, Spence & Lachlan, 2012). Similar effects on receivers have been detected have been noted that when measured immediately after exposure to stimuli with varying exemplars. (Spence, Westerman & Rice, 2017). However, when the outcomes were measured two weeks later, those exposed to more threatening exemplified portrayals reported higher threats perceptions indicated more intentions to change behavior. Thus, the impact of the exemplar, was dormant and was only detected later.
For the current study, the no-name condition and the absence of other information may create a circumstance where people may think back to what they remember most about storms. For example, storms are often portrayed as dangerous and intense in the media. Therefore, this is what is available from memory (the past exemplars) and are used to make this judgment because other information is absent. This could be called the “Weather Channel heuristic,” because in the absence of any other information, people might remember, recall, and apply these types of portrayals to the unnamed storm. This may explain the distributions of the means for perceptions of severity.
The sleeper effect in exemplification research needs further inquiry. It is possible that after a period of time, the effects storm naming may emerge in the original anticipated direction; alternately the sleeper effect explanation offered here may hold. More research is needed on the sleeper effect to determine the amount of time needed after exposure to a stimulus for the effect to emerge. Also, the sleeper effect needs to be examined in a social media environment. If in the case of named storms, the sleeper effect only needs minutes or hours to evoke heuristic processing, this would provide support for the naming of storms using exemplars and suggest new areas of research with exemplification and weather events through social media.