A Multistage Analysis of Predicting Public Resilience of Impactful Social Media Crisis Communication in Flooding Emergencies

The desire of people affected by crises or disasters is to return to their normal lives quickly and easily. Social media provides a platform for crisis response which helps recovery and resilience building. Therefore, this study aims to investigate how social media crisis response and information shaped people's resilience-building, either affected by the flooding or not. Data collected in Malaysia after flooding consist of 375 observations. A multi-stage analysis which consists of partial least square structural equation modeling (PLS-SEM), partial least square predictions algorithm (PLS-predict), and artificial neural networks (ANN), are employed to examine the outcome of social media crisis communications. The result shows support for the significance of crisis, crisis response, social media interaction, and information seeking and sharing are not. Furthermore, the predictive relevance of the model is strong, and the root mean square error (RMSE) obtained from ANN analysis indicated a predictive model capacity. Hence, the findings demonstrated the impact of social media crisis responses that crisis management and communication may use in decision-making.