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Data from: Characters matter: how narratives shape affective responses to risk communication

Citation

Shanahan, Elizabeth A. et al. (2019), Data from: Characters matter: how narratives shape affective responses to risk communication, Dryad, Dataset, https://doi.org/10.5061/dryad.b8gtht784

Abstract

Introduction

Whereas scientists depend on the language of probability to relay information about hazards, risk communication may be more effective when embedding scientific information in narratives. The persuasive power of narratives is theorized to reside, in part, in narrative transportation.

Purpose

This study seeks to advance the science of stories in risk communication by measuring real-time affective responses as a proxy indicator for narrative transportation during science messages that present scientific information in the context of narrative.

Methods

This study employed a within-subjects design in which participants (n = 90) were exposed to eight science messages regarding flood risk. Conventional science messages using probability and certainty language represented two conditions. The remaining six conditions were narrative science messages that embedded the two conventional science messages within three story forms that manipulated the narrative mechanism of character selection. Informed by the Narrative Policy Framework, the characters portrayed in the narrative science messages were hero, victim, and victim-to-hero. Natural language processing techniques were applied to identify and rank hero and victim vocabularies from 45 resident interviews conducted in the study area; the resulting classified vocabulary was used to build each of the three story types. Affective response data were collected over 12 group sessions across three flood-prone communities in Montana. Dial response technology was used to capture continuous, second-by-second recording of participants’ affective responses while listening to each of the eight science messages. Message order was randomized across sessions. ANOVA and three linear mixed-effects models were estimated to test our predictions.

Results

First, both probabilistic and certainty science language evoked negative affective responses with no statistical differences between them. Second, narrative science messages were associated with greater variance in affective responses than conventional science messages. Third, when characters are in action, variation in the narrative mechanism of character selection leads to significantly different affective responses. Hero and victim-to-hero characters elicit positive affective responses, while victim characters produce a slightly negative response.

Conclusions

In risk communication, characters matter in audience experience of narrative transportation as measured by affective responses.