| Literature DB >> 33909699 |
Francesca Panzeri1, Simona Di Paola2, Filippo Domaneschi2.
Abstract
In recent times, many alarm bells have begun to sound: the metaphorical presentation of the COVID-19 emergency as a war might be dangerous, because it could affect the way people conceptualize the pandemic and react to it, leading citizens to endorse authoritarianism and limitations to civil liberties. The idea that conceptual metaphors actually influence reasoning has been corroborated by Thibodeau and Boroditsky, who showed that, when crime is metaphorically presented as a beast, readers become more enforcement-oriented than when crime is metaphorically framed as a virus. Recently, Steen, Reijnierse and Burgers replied that this metaphorical framing effect does not seem to occur and suggested that the question should be rephrased about the conditions under which metaphors do or do not influence reasoning. In this paper, we investigate whether presenting the COVID-19 pandemic as a war affects people's reasoning about the pandemic. Data collected suggest that the metaphorical framing effect does not occur by default. Rather, socio-political individual variables such as speakers' political orientation and source of information favor the acceptance of metaphor congruent entailments: right-wing participants and participants relying on independent sources of information are those more conditioned by the COVID-19 war metaphor, thus more inclined to prefer bellicose options.Entities:
Mesh:
Year: 2021 PMID: 33909699 PMCID: PMC8081452 DOI: 10.1371/journal.pone.0250651
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Fig 1Transliteration in English of the scenario “Fake News” with the relative options.
The metaphorical and the neutral text differed in the expressions in bold displayed within square brackets.
Descriptive and inferential statistics for the set of socio-demographic measures.
| Gender | 64.5 (129) | 63.7 (70) | 61.5 (59) | χ 2(1) = 0.74 | .38 | |
| 35.5 (71) | 32.7 (34) | 38.5 (37) | ||||
| Education Level | 4.5 (9) | 2.8 (3) | 6.3 (6) | χ 2(3) = 6.05 | .10 | |
| 20.8 (42) | 23.6 (25) | 17.7 (17) | ||||
| 57.4 (116) | 61.3 (65) | 53.1 (51) | ||||
| 17.3 (35) | 12.3 (13) | 22.9 (22) | ||||
| Region SARS-CoV-2 Risk | 84.9 (169) | 86.5 (90) | 83.2 (79) | χ 2(2) = 0.87 | .64 | |
| 9.5 (19) | 7.7 (8) | 11.6 (11) | ||||
| 5.5 (11) | 5.8 (6) | 5.3 (5) | ||||
| Age | 33.53(14.77) | 32.54(14.63) | 34.62(14.93) | 4733.5 | .39 | |
| TV_Newspaper | 6.41(1.80) | 6.41(1.94) | 6.41(1.65) | 3839.5 | .85 | |
| Ind_Social | 6.04(2.03) | 6.13(2.02) | 5.94(2.05) | 3932 | .64 | |
| Political orientation | 3.04(1.77) | 3.02(1.88) | 3.05(1.66) | 3632 | .64 | |
| Approval Premier | 6.94(1.84) | 6.95(1.66) | 6.94(2.03) | 3671.5 | .73 | |
| Approval Government | 5.92(1.90) | 5.86(1.90) | 5.98(1.91) | 3530 | .44 | |
| Approval Opposition | 3.54(1.96) | 3.55(2.04) | 3.53(1.89) | 3770.5 | .97 | |
| Approval Region Governor | 5.81(2.88) | 6.01(2.90) | 5.95(2.87) | 4137.5 | .27 | |
Gender, Education level (A: middle school diploma; B: secondary school certificate; C: University student; D: Degree) and Region SARS-CoV-2 Risk (High, Medium, Low): frequency distribution in total and by condition; chi-squared statistics for differences between conditions. All other measures: mean (SD) in total and by condition; Wilcoxon signed rank test statistics for differences between conditions.
Multiple regression statistics for the final overall model.
| Overall Model | Start AIC | Final AIC | F | R2 | ΔR2 | p | ||||
|---|---|---|---|---|---|---|---|---|---|---|
| 282.63 | 268.46 | (20,162) = 3.26 | 0.28 | 0.19 | < .0001* | |||||
| Estimate ( | Std. Error ( | t | p | β | (G)VIF | GVIF1/(2*df) | Mean (G)VIF | Auto-correlation | ||
| Intercept | 0.66 | 2.19 | 0.30 | .76 | 0 | |||||
| Condition | -1.06 | 3.25 | -0.32 | .74 | -0.24 | 124.54 | 11.15 | |||
| Gender | 0.49 | 0.32 | 1.53 | .12 | 0.10 | 1.12 | 1.06 | |||
| Age | -0.02 | 0.02 | -1 | .31 | -0.19 | 8.42 | 2.90 | |||
| Education level | B | -0.98 | 1.30 | -0.75 | .44 | -0.17 | 47.38 | 1.90 | ||
| C | -1.90 | 1.59 | -1.20 | .23 | -0.42 | |||||
| D | -0.73 | 1.34 | -0.54 | .58 | -0.12 | |||||
| Region Covid Risk | Low | -0.66 | 0.64 | -1.02 | .30 | -0.07 | 1.29 | 1.06 | ||
| Medium | -1.61 | 0.53 | -3.01 | .002* | -0.21 | |||||
| TV_Newspaper | 0.11 | 0.11 | 1.02 | .30 | 0.09 | 1.92 | 1.38 | |||
| Ind_Social | 0.15 | 0.07 | 2.03 | .04* | 0.14 | 1.14 | 1.06 | |||
| Political Orientation | 0.46 | 0.12 | 3.77 | .0002** | 0.38 | 2.35 | 1.53 | |||
| Approval Premier | 0.20 | 0.08 | 2.39 | .01* | 0.17 | 1.28 | 1.13 | |||
| Approval Opposition | -0.13 | 0.08 | -1.58 | .11 | -0.12 | 1.44 | 1.20 | |||
| Approval Region Governor | 0.19 | 0.06 | 3.27 | .001*** | 0.26 | 1.46 | 1.20 | |||
| Condition:Age | 0.06 | 0.04 | 1.35 | .17 | 0.59 | 43.36 | 6.58 | |||
| Cond:Education | Cond:B | 2.78 | 1.84 | 1.51 | .13 | 0.35 | 1030.73 | 3.17 | ||
| Cond:C | 3.50 | 2.42 | 1.44 | .14 | 0.70 | |||||
| Cond:D | 0.94 | 1.91 | 0.49 | .62 | 0.13 | |||||
| Condition:TV_Newspaper | -0.45 | 0.17 | -2.57 | .01* | -0.70 | 17.05 | 4.12 | |||
| Condition:Political Orientation | -0.42 | 0.16 | -2.56 | .01* | -0.38 | 5.20 | 2.28 | |||
| 30.01 | DW = 1.71; p = .04 | |||||||||
The table displays the multiple regression statistics for the final overall model (i.e., outcome variable: Total score in conditions metaphoric and neutral of the metaphor task) as resulting from the backward procedure and coefficient values.
Multiple regression statistics of the neutral model.
| Model Neutral | Start AIC | Final AIC | F | R2 | ΔR2 | p | ||
|---|---|---|---|---|---|---|---|---|
| 149.59 | 137.75 | (2,86) = 5.29 | 0.12 | 0.10 | .002** | |||
| Estimate ( | Std. Error ( | t | p | β | VIF | Mean VIF | Autocorrelation | |
| Intercept | 3.16 | 0.98 | 3.21 | .001*** | 0 | |||
| TV_Newspaper | -0.25 | 0.13 | -1.82 | .07 | -0.18 | 1 | ||
| Approval Region Governor | 0.25 | 0.07 | 3.22 | .001*** | 0.32 | 1 | ||
| 1 | DW = 1.74; p = .23 |
The table reports the multiple regression statistics separated by condition as resulting from the backward procedure and coefficient values. Model Neutral: Outcome variable = total score in condition neutral.
Multiple regression statistics of the metaphoric model.
| Model Metaphoric | Start AIC | Final AIC | F | R2 | ΔR2 | p | ||
|---|---|---|---|---|---|---|---|---|
| 135.43 | 125.72 | (4,89) = 7.27 | 0.24 | 0.21 | < .0001*** | |||
| Estimate ( | Std. Error ( | t | p | β | VIF | Mean VIF | Autocorrelation | |
| Intercept | -1.59 | 1.16 | -1.37 | .17 | 0 | |||
| Ind_Social | 0.21 | 0.09 | 2.18 | .03* | 0.20 | 1.04 | ||
| Political orientation | 0.44 | 0.11 | 3.84 | .0002*** | 0.38 | 1.17 | ||
| Approval Premier | 0.23 | 0.12 | 1.88 | .06 | 0.18 | 1.10 | ||
| Approval Region Governor | 0.12 | 0.07 | 1.78 | .07 | 0.17 | 1.08 | ||
| 1.10 | DW = 1.85; p = .46 |
The table reports the multiple regression statistics separated by condition as resulting from the backward procedure and coefficient values. Model Metaphoric: outcome variable = total score in condition metaphor.
Fig 2Predictors of participants’ score in the metaphor task.
Top: Correlations between participants’ score in the metaphor condition and their political orientation (left panel) and the frequency rating (composite score) for independent information channels and social networks (right panel). Bottom: Correlation between participants’ score in the neutral condition and their Region governor’s approval.