Fear, Hope, and COVID-19: Emotional Elite Rhetoric and Its Impact on the Public During the First Wave of the COVID-19 Pandemic

TY - JOUR

T1 - Fear, Hope, and COVID-19

T2 - Emotional Elite Rhetoric and Its Impact on the Public During the First Wave of the COVID-19 Pandemic

AU - Widmann, Tobias

PY - 2022/10

Y1 - 2022/10

N2 - Research shows that emotions matter in politics, and they matter during a public health crisis. Yet, a comprehensive analysis of emotional political rhetoric during the COVID-19 crisis is still missing. Based on parties' position in the political arena (government versus populist radical parties), I expect differences in how specific emotions are employed and in how these messages actually influence the public. To test my hypotheses, I use word embeddings and neural network classifiers to measure fear and hope appeals in social media messages of political parties in four European countries. Furthermore, I rely on more than 1,400,000 public tweets of random citizens to estimate the impact of party messages. To do so, I employ vector autoregression (VAR) analysis to compare retweet volumes of political messages to emotional expressions in public tweets. Results indicate two main findings, (1) populist radical parties communicate less about the pandemic and decrease fear and increase hope appeals while COVID case numbers are rising whereas government parties exhibit the opposite pattern; (2) increased diffusion of party tweets consistently precedes change in partisans' emotional expressions the following day. The findings can carry important implications for (affective) polarization and the level of protective behavior among the population.

AB - Research shows that emotions matter in politics, and they matter during a public health crisis. Yet, a comprehensive analysis of emotional political rhetoric during the COVID-19 crisis is still missing. Based on parties' position in the political arena (government versus populist radical parties), I expect differences in how specific emotions are employed and in how these messages actually influence the public. To test my hypotheses, I use word embeddings and neural network classifiers to measure fear and hope appeals in social media messages of political parties in four European countries. Furthermore, I rely on more than 1,400,000 public tweets of random citizens to estimate the impact of party messages. To do so, I employ vector autoregression (VAR) analysis to compare retweet volumes of political messages to emotional expressions in public tweets. Results indicate two main findings, (1) populist radical parties communicate less about the pandemic and decrease fear and increase hope appeals while COVID case numbers are rising whereas government parties exhibit the opposite pattern; (2) increased diffusion of party tweets consistently precedes change in partisans' emotional expressions the following day. The findings can carry important implications for (affective) polarization and the level of protective behavior among the population.

KW - computational text analysis

KW - covid-19

KW - emotions

KW - political communication

KW - text-as-data

UR - https://www.scopus.com/pages/publications/85129921511

U2 - 10.1111/pops.12831

DO - 10.1111/pops.12831

M3 - Journal article

C2 - 35941918

SN - 0162-895X

VL - 43

SP - 827

EP - 850

JO - Political Psychology

JF - Political Psychology

IS - 5

ER -