Is it too late now to say we’re sorry? Examining anxiety contagion and crisis communication strategies using machine learning

Gritjon Gruda; Adegboyega Ojo

doi:10.1371/journal.pone.0274539

Is it too late now to say we’re sorry? Examining anxiety contagion and crisis communication strategies using machine learning

Abstract

In this paper, we explore the role of perceived emotions and crisis communication strategies via organizational computer-mediated communication in predicting public anxiety, the default crisis emotion. We use a machine-learning approach to detect and predict anxiety scores in organizational crisis announcements on social media and the public’s responses to these posts. We also control for emotional and language tones in organizational crisis responses using a separate machine learning algorithm. Perceived organizational anxiety positively influences public anxiety, confirming the occurrence of emotional contagion from the organization to the public. Crisis response strategies moderated this relationship, so that responsibility acknowledgment lowered public anxiety the most. We argue that by accounting for emotions expressed in organizational crisis responses, organizations may be able to better predict and manage public emotions.

Citations

2

CrossRef
0

Web of Science
1

Scopus

Authors (2)

Gritjon Gruda Dr
Adegboyega Ojo dr lic.

Cite as

Full text

download paper

downloaded 20 times

Publication version: Accepted or Published Version
DOI:: Digital Object Identifier (open in new tab) 10.1371/journal.pone.0274539
License: open in new tab

full content of the article see on external site open in new tab

Keywords

Details

Category:

Articles

Type:

artykuły w czasopismach

Published in:

PLOS ONE no. 17, pages 1 - 12,
ISSN: 1932-6203

Language:

English

Publication year:

2022

Bibliographic description:

Gruda G., Ojo A.: Is it too late now to say we’re sorry? Examining anxiety contagion and crisis communication strategies using machine learning// PLOS ONE -Vol. 17,iss. 9 (2022), s.1-12

DOI: