Sentiment evolution of online public opinion of emergency situations in railway stations: a case study of Wuhan railway stations

Abstract

Preventing secondary crises resulting from emergency incidents in engineering projects is a crucial and complex task for project operation management. Public opinion and its underlying sentiment can act as reliable indicators, reflecting the progression of emergency incidents, and warrant serious consideration. With the advent of Web 2.0, the management of online public opinion (OPO) through social platforms has advanced significantly. However, previous research has overlooked the diverse categories of participants contributing to OPO evolution. This article proposes an optimised bounded confidence model (BCM) for sentiment OPO evolution under emergency situations at railway stations, incorporating multiple participant categories. A conceptual model based on eleven assumptions is developed, involving four key participants (netizens, media, opinion leaders, and government) structured into four sub-processes. To illustrate this model, the case of the Wuhan railway stations’ blockade during the COVID-19 outbreak is examined. This case study demonstrates the initial data acquisition and simulation process. The standard simulation results are recorded, followed by a multiple-sensitivity analysis to investigate the impact of various critical factor combinations on OPO evolution. Finally, policy recommendations are provided to government departments to enhance their response to emergency situations, particularly those involving railway stations, thereby ensuring public safety.