Characters matter: How narratives shape affective responses to risk communication

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.

Fig 1. Map of Montana, USA, with Yellowstone River and three study communities.

Fig 2. Dial device used to collect second-by-second affective responses.

Fig 3. Illustration of how T_Net and T_SLR were calculated.

Points represent the dial reading at each second for a participant during the Characters in action segment of the Victim—to—hero message with Certainty language. Gray line traces points. Solid black line denotes simple linear regression of dial reading against elapsed time of message. T_Net was calculated by subtracting the participant’s initial dial reading from their final dial reading within a segment. T_SLR was calculated by multiplying the estimated change in dial reading per second (regression slope) by the duration of the segment in seconds.

Fig 4. Mean standard deviation of dial readings by science message type.

Point estimates and 95% confidence intervals were generated from marginal means of the linear mixed-effects models as described in Methods: Statistical analyses. Letters adjacent to point estimates denote statistically significant differences.

Fig 5. Transportation by dial readings with T_Net and T_SLR.

(A) Mean dial readings of participants for each science message by segment. (B) Estimates of mean change in dial readings and 95% confidence intervals by segment. Negative affective responses fall below zero and positive affective responses above zero. The calculation of transportation is indicated by Net for T_Net and SLR for T_SLR as described in Methods: Data and measures of transportation. Point estimates and 95% confidence intervals were generated from marginal means of the linear mixed-effects models as described in Methods: Statistical analyses. Letters adjacent to point estimates denote statistically significant differences.