Revisiting Social Media Crisis Communication Model for Building Resilience via Artificial Neural Network Analysis

Abstract

The devastating effects of crises and how social media usage assists or informs stakeholders are attracting attention recently. This study explored the literature on social media crisis communication and resilience and evaluated the predictors of public resilience and the impact of social media activities on public resilience. Using the artificial neural network (ANN) approach, this study sought to determine the effect of the crisis, crisis response, and social media interaction on public resilience. The ANN evaluates the mean value of the normalized performance from ten neural networks generated through cross-validation. The results indicate crisis response and social media interaction as the most significant predictors of resilience. Also, the findings show that it is desirable to continue studying the public's roles, motives, and actions on social media. The study offered theoretical justification to advance effective social media crisis management and communication.